Related
This is my problem description:
"According to the Survey on Household Income and Wealth, we need to find out the top 10% households with the most income and expenditures. However, we know that these collected data is not reliable due to many misstatements. Despite these misstatements, we have some features in the dataset which are certainly reliable. But these certain features are just a little part of information for each household wealth."
Unreliable data means that households tell lies to government. These households misstate their income and wealth in order to unfairly get more governmental services. Therefore, these fraudulent statements in original data will lead to incorrect results and patterns.
Now, I have below questions:
How should we deal with unreliable data in data science?
Is there any way to figure out these misstatements and then report the top 10% rich people with better accuracy using Machine Learning algorithms?
-How can we evaluate our errors in this study? Since we have unlabeled dataset, should I look for labeling techniques? Or, should I use unsupervised methods? Or, should I work with semi-supervised learning methods?
Is there any idea or application in Machine Learning which tries to improve the quality of collected data?
Please introduce me any ideas or references which can help me in this issue.
Thanks in advance.
Q: How should we deal with unreliable data in data science
A: Use feature engineering to fix unreliable data (make some transformations on unreliable data to make it reliable) or drop them out completely - bad features could significantly decrease the quality of the model
Q: Is there any way to figure out these misstatements and then report the top 10% rich people with better accuracy using Machine Learning algorithms?
A: ML algorithms are not magic sticks, they can't figure out anything unless you tell them what you are looking for. Can you describe what means 'unreliable'? If yes, you can, as I mentioned, use feature engineering or write a code which will fix the data. Otherwise no ML algorithm will be able to help you, without the description of what exactly you want to achieve
Q: Is there any idea or application in Machine Learning which tries to improve the quality of collected data?
A: I don't think so just because the question itself is too open-ended. What means 'the quality of the data'?
Generally, here are couple of things for you to consider:
1) Spend some time on googling feature engineering guides. They cover how to prepare your data for you ML algorithms, refine it, fix it. Good data with good features dramatically increase the results.
2) You don't need to use all of features from original data. Some of features of original dataset are meaningless and you don't need to use them. Try to run gradient boosting machine or random forest classifier from scikit-learn on your dataset to perform classification (or regression, if you do regression). These algorithms also evaluate importance of each feature of original dataset. Part of your features will have extremely low importance for classification, so you may wish to drop them out completely or try to combine unimportant features together somehow to produce something more important.
I am going to do a research project which involves predicting imminent failure of an engine using time data obtained from sensors. The data basically contains the readings of various embedded sensors every 10 minutes for many months. Such data is available for about 100 or so different units (all are the same engine model), along with the time of failure.
While I do have a reasonably good understanding of Machine Learning, I am at a loss of approaching this. I have done a few projects that involved static datasets (using SVMs, Neural Nets, Logistic Regression etc.) and even one on predicting time series. But this is quite different. While the project involves time data, it is hardly a matter of predicting the future values. Rather it is a case of anomaly detection on sequential time data.
Please could you give some ideas as to how I could approach it?
I'm particularly interested in Neural Networks/ Deep Learning, so any ideas on using them for this task would also be welcome. I would prefer to use Python or R, although I would be open to using something else if it was particularly geared for this sort of task.
Also could you give me some formal terms using which I could search for relevant literature?
Thanks
As a general comment, try hard to express everything that you know about the physical system in a model, then use that model for inference. I worked on such problems in my dissertation: Unified Prediction and Diagnosis in Engineering Systems by means of Distributed Belief Networks (see chapter 6). I can say more if you provide additional details about your problem domain.
Don't expect general machine learning models (neural networks, SVM, etc) to figure out the structure of the problem for you. Having the right form of the model is much, much more important than having a general model + lots of data -- this is the summary of my experience.
I want to teach myself enough machine learning so that I can, to begin with, understand enough to put to use available open source ML frameworks that will allow me to do things like:
Go through the HTML source of pages
from a certain site and "understand"
which sections form the content,
which the advertisements and which
form the metadata ( neither the
content, nor the ads - for eg. -
TOC, author bio etc )
Go through the HTML source of pages
from disparate sites and "classify"
whether the site belongs to a
predefined category or not ( list of
categories will be supplied
beforhand )1.
... similar classification tasks on
text and pages.
As you can see, my immediate requirements are to do with classification on disparate data sources and large amounts of data.
As far as my limited understanding goes, taking the neural net approach will take a lot of training and maintainance than putting SVMs to use?
I understand that SVMs are well suited to ( binary ) classification tasks like mine, and open source framworks like libSVM are fairly mature?
In that case, what subjects and topics
does a computer science graduate need
to learn right now, so that the above
requirements can be solved, putting
these frameworks to use?
I would like to stay away from Java, is possible, and I have no language preferences otherwise. I am willing to learn and put in as much effort as I possibly can.
My intent is not to write code from scratch, but, to begin with putting the various frameworks available to use ( I do not know enough to decide which though ), and I should be able to fix things should they go wrong.
Recommendations from you on learning specific portions of statistics and probability theory is nothing unexpected from my side, so say that if required!
I will modify this question if needed, depending on all your suggestions and feedback.
"Understanding" in machine learn is the equivalent of having a model. The model can be for example a collection of support vectors, the layout and weights of a neural network, a decision tree, or more. Which of these methods work best really depends on the subject you're learning from and on the quality of your training data.
In your case, learning from a collection of HTML sites, you will like to preprocess the data first, this step is also called "feature extraction". That is, you extract information out of the page you're looking at. This is a difficult step, because it requires domain knowledge and you'll have to extract useful information, or otherwise your classifiers will not be able to make good distinctions. Feature extraction will give you a dataset (a matrix with features for each row) from which you'll be able to create your model.
Generally in machine learning it is advised to also keep a "test set" that you do not train your models with, but that you will use at the end to decide on what is the best method. It is of extreme importance that you keep the test set hidden until the very end of your modeling step! The test data basically gives you a hint on the "generalization error" that your model is making. Any model with enough complexity and learning time tends to learn exactly the information that you train it with. Machine learners say that the model "overfits" the training data. Such overfitted models seem to appear good, but this is just memorization.
While software support for preprocessing data is very sparse and highly domain dependent, as adam mentioned Weka is a good free tool for applying different methods once you have your dataset. I would recommend reading several books. Vladimir Vapnik wrote "The Nature of Statistical Learning Theory", he is the inventor of SVMs. You should get familiar with the process of modeling, so a book on machine learning is definitely very useful. I also hope that some of the terminology might be helpful to you in finding your way around.
Seems like a pretty complicated task to me; step 2, classification, is "easy" but step 1 seems like a structure learning task. You might want to simplify it to classification on parts of HTML trees, maybe preselected by some heuristic.
The most widely used general machine learning library (freely) available is probably WEKA. They have a book that introduces some ML concepts and covers how to use their software. Unfortunately for you, it is written entirely in Java.
I am not really a Python person, but it would surprise me if there aren't also a lot of tools available for it as well.
For text-based classification right now Naive Bayes, Decision Trees (J48 in particular I think), and SVM approaches are giving the best results. However they are each more suited for slightly different applications. Off the top of my head I'm not sure which would suit you the best. With a tool like WEKA you could try all three approaches with some example data without writing a line of code and see for yourself.
I tend to shy away from Neural Networks simply because they can get very very complicated quickly. Then again, I haven't tried a large project with them mostly because they have that reputation in academia.
Probability and statistics knowledge is only required if you are using probabilistic algorithms (like Naive Bayes). SVMs are generally not used in a probabilistic manner.
From the sound of it, you may want to invest in an actual pattern classification textbook or take a class on it in order to find exactly what you are looking for. For custom/non-standard data sets it can be tricky to get good results without having a survey of existing techniques.
It seems to me that you are now entering machine learning field, so I'd really like to suggest to have a look at this book: not only it provides a deep and vast overview on the most common machine learning approaches and algorithms (and their variations) but it also provides a very good set of exercises and scientific paper links. All of this is wrapped in an insightful language starred with a minimal and yet useful compendium about statistics and probability
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What is machine learning ?
What does machine learning code do ?
When we say that the machine learns, does it modify the code of itself or it modifies history (database) which will contain the experience of code for given set of inputs?
What is a machine learning ?
Essentially, it is a method of teaching computers to make and improve predictions or behaviors based on some data. What is this "data"? Well, that depends entirely on the problem. It could be readings from a robot's sensors as it learns to walk, or the correct output of a program for certain input.
Another way to think about machine learning is that it is "pattern recognition" - the act of teaching a program to react to or recognize patterns.
What does machine learning code do ?
Depends on the type of machine learning you're talking about. Machine learning is a huge field, with hundreds of different algorithms for solving myriad different problems - see Wikipedia for more information; specifically, look under Algorithm Types.
When we say machine learns, does it modify the code of itself or it modifies history (Data Base) which will contain the experience of code for given set of inputs ?
Once again, it depends.
One example of code actually being modified is Genetic Programming, where you essentially evolve a program to complete a task (of course, the program doesn't modify itself - but it does modify another computer program).
Neural networks, on the other hand, modify their parameters automatically in response to prepared stimuli and expected response. This allows them to produce many behaviors (theoretically, they can produce any behavior because they can approximate any function to an arbitrary precision, given enough time).
I should note that your use of the term "database" implies that machine learning algorithms work by "remembering" information, events, or experiences. This is not necessarily (or even often!) the case.
Neural networks, which I already mentioned, only keep the current "state" of the approximation, which is updated as learning occurs. Rather than remembering what happened and how to react to it, neural networks build a sort of "model" of their "world." The model tells them how to react to certain inputs, even if the inputs are something that it has never seen before.
This last ability - the ability to react to inputs that have never been seen before - is one of the core tenets of many machine learning algorithms. Imagine trying to teach a computer driver to navigate highways in traffic. Using your "database" metaphor, you would have to teach the computer exactly what to do in millions of possible situations. An effective machine learning algorithm would (hopefully!) be able to learn similarities between different states and react to them similarly.
The similarities between states can be anything - even things we might think of as "mundane" can really trip up a computer! For example, let's say that the computer driver learned that when a car in front of it slowed down, it had to slow down to. For a human, replacing the car with a motorcycle doesn't change anything - we recognize that the motorcycle is also a vehicle. For a machine learning algorithm, this can actually be surprisingly difficult! A database would have to store information separately about the case where a car is in front and where a motorcycle is in front. A machine learning algorithm, on the other hand, would "learn" from the car example and be able to generalize to the motorcycle example automatically.
Machine learning is a field of computer science, probability theory, and optimization theory which allows complex tasks to be solved for which a logical/procedural approach would not be possible or feasible.
There are several different categories of machine learning, including (but not limited to):
Supervised learning
Reinforcement learning
Supervised Learning
In supervised learning, you have some really complex function (mapping) from inputs to outputs, you have lots of examples of input/output pairs, but you don't know what that complicated function is. A supervised learning algorithm makes it possible, given a large data set of input/output pairs, to predict the output value for some new input value that you may not have seen before. The basic method is that you break the data set down into a training set and a test set. You have some model with an associated error function which you try to minimize over the training set, and then you make sure that your solution works on the test set. Once you have repeated this with different machine learning algorithms and/or parameters until the model performs reasonably well on the test set, then you can attempt to use the result on new inputs. Note that in this case, the program does not change, only the model (data) is changed. Although one could, theoretically, output a different program, but that is not done in practice, as far as I am aware. An example of supervised learning would be the digit recognition system used by the post office, where it maps the pixels to labels in the set 0...9, using a large set of pictures of digits that were labeled by hand as being in 0...9.
Reinforcement Learning
In reinforcement learning, the program is responsible for making decisions, and it periodically receives some sort of award/utility for its actions. However, unlike in the supervised learning case, the results are not immediate; the algorithm could prescribe a large sequence of actions and only receive feedback at the very end. In reinforcement learning, the goal is to build up a good model such that the algorithm will generate the sequence of decisions that lead to the highest long term utility/reward. A good example of reinforcement learning is teaching a robot how to navigate by giving a negative penalty whenever its bump sensor detects that it has bumped into an object. If coded correctly, it is possible for the robot to eventually correlate its range finder sensor data with its bumper sensor data and the directions that sends to the wheels, and ultimately choose a form of navigation that results in it not bumping into objects.
More Info
If you are interested in learning more, I strongly recommend that you read Pattern Recognition and Machine Learning by Christopher M. Bishop or take a machine learning course. You may also be interested in reading, for free, the lecture notes from CIS 520: Machine Learning at Penn.
Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. Read more on Wikipedia
Machine learning code records "facts" or approximations in some sort of storage, and with the algorithms calculates different probabilities.
The code itself will not be modified when a machine learns, only the database of what "it knows".
Machine learning is a methodology to create a model based on sample data and use the model to make a prediction or strategy. It belongs to artificial intelligence.
Machine learning is simply a generic term to define a variety of learning algorithms that produce a quasi learning from examples (unlabeled/labeled). The actual accuracy/error is entirely determined by the quality of training/test data you provide to your learning algorithm. This can be measured using a convergence rate. The reason you provide examples is because you want the learning algorithm of your choice to be able to informatively by guidance make generalization. The algorithms can be classed into two main areas supervised learning(classification) and unsupervised learning(clustering) techniques. It is extremely important that you make an informed decision on how you plan on separating your training and test data sets as well as the quality that you provide to your learning algorithm. When you providing data sets you want to also be aware of things like over fitting and maintaining a sense of healthy bias in your examples. The algorithm then basically learns wrote to wrote on the basis of generalization it achieves from the data you have provided to it both for training and then for testing in process you try to get your learning algorithm to produce new examples on basis of your targeted training. In clustering there is very little informative guidance the algorithm basically tries to produce through measures of patterns between data to build related sets of clusters e.g kmeans/knearest neighbor.
some good books:
Introduction to ML (Nilsson/Stanford),
Gaussian Process for ML,
Introduction to ML (Alpaydin),
Information Theory Inference and Learning Algorithms (very useful book),
Machine Learning (Mitchell),
Pattern Recognition and Machine Learning (standard ML course book at Edinburgh and various Unis but relatively a heavy reading with math),
Data Mining and Practical Machine Learning with Weka (work through the theory using weka and practice in Java)
Reinforcement Learning there is a free book online you can read:
http://www.cs.ualberta.ca/~sutton/book/ebook/the-book.html
IR, IE, Recommenders, and Text/Data/Web Mining in general use alot of Machine Learning principles. You can even apply Metaheuristic/Global Optimization Techniques here to further automate your learning processes. e.g apply an evolutionary technique like GA (genetic algorithm) to optimize your neural network based approach (which may use some learning algorithm). You can approach it purely in form of a probablistic machine learning approach for example bayesian learning. Most of these algorithms all have a very heavy use of statistics. Concepts of convergence and generalization are important to many of these learning algorithms.
Machine learning is the study in computing science of making algorithms that are able to classify information they haven't seen before, by learning patterns from training on similar information. There are all sorts of kinds of "learners" in this sense. Neural networks, Bayesian networks, decision trees, k-clustering algorithms, hidden markov models and support vector machines are examples.
Based on the learner, they each learn in different ways. Some learners produce human-understandable frameworks (e.g. decision trees), and some are generally inscrutable (e.g. neural networks).
Learners are all essentially data-driven, meaning they save their state as data to be reused later. They aren't self-modifying as such, at least in general.
I think one of the coolest definitions of machine learning that I've read is from this book by Tom Mitchell. Easy to remember and intuitive.
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E
Shamelessly ripped from Wikipedia: Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases.
Quite simply, machine learning code accomplishes a machine learning task. That can be a number of things from interpreting sensor data to a genetic algorithm.
I would say it depends. No, modifying code is not normal, but is not outside the realm of possibility. I would also not say that machine learning always modifies a history. Sometimes we have no history to build off of. Sometime we simply want to react to the environment, but not actually learn from our past experiences.
Basically, machine learning is a very wide-open discipline that contains many methods and algorithms that make it impossible for there to be 1 answer to your 3rd question.
Machine learning is a term that is taken from the real world of a person, and applied on something that can't actually learn - a machine.
To add to the other answers - machine learning will not (usually) change the code, but it might change it's execution path and decision based on previous data or new gathered data and hence the "learning" effect.
there are many ways to "teach" a machine - you give weights to many parameter of an algorithm, and then have the machine solve it for many cases, each time you give her a feedback about the answer and the machine adjusts the weights according to how close the machine answer was to your answer or according to the score you gave it's answer, or according to some results test algorithm.
This is one way of learning and there are many more...
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I am wanting some expert guidance here on what the best approach is for me to solve a problem. I have investigated some machine learning, neural networks, and stuff like that. I've investigated weka, some sort of baesian solution.. R.. several different things. I'm not sure how to really proceed, though. Here's my problem.
I have, or will have, a large collection of events.. eventually around 100,000 or so. Each event consists of several (30-50) independent variables, and 1 dependent variable that I care about. Some independent variables are more important than others in determining the dependent variable's value. And, these events are time relevant. Things that occur today are more important than events that occurred 10 years ago.
I'd like to be able to feed some sort of learning engine an event, and have it predict the dependent variable. Then, knowing the real answer for the dependent variable for this event (and all the events that have come along before), I'd like for that to train subsequent guesses.
Once I have an idea of what programming direction to go, I can do the research and figure out how to turn my idea into code. But my background is in parallel programming and not stuff like this, so I'd love to have some suggestions and guidance on this.
Thanks!
Edit: Here's a bit more detail about the problem that I'm trying to solve: It's a pricing problem. Let's say that I'm wanting to predict prices for a random comic book. Price is the only thing I care about. But there are lots of independent variables one could come up with. Is it a Superman comic, or a Hello Kitty comic. How old is it? What's the condition? etc etc. After training for a while, I want to be able to give it information about a comic book I might be considering, and have it give me a reasonable expected value for the comic book. OK. So comic books might be a bogus example. But you get the general idea. So far, from the answers, I'm doing some research on Support vector machines and Naive Bayes. Thanks for all of your help so far.
Sounds like you're a candidate for Support Vector Machines.
Go get libsvm. Read "A practical guide to SVM classification", which they distribute, and is short.
Basically, you're going to take your events, and format them like:
dv1 1:iv1_1 2:iv1_2 3:iv1_3 4:iv1_4 ...
dv2 1:iv2_1 2:iv2_2 3:iv2_3 4:iv2_4 ...
run it through their svm-scale utility, and then use their grid.py script to search for appropriate kernel parameters. The learning algorithm should be able to figure out differing importance of variables, though you might be able to weight things as well. If you think time will be useful, just add time as another independent variable (feature) for the training algorithm to use.
If libsvm can't quite get the accuracy you'd like, consider stepping up to SVMlight. Only ever so slightly harder to deal with, and a lot more options.
Bishop's Pattern Recognition and Machine Learning is probably the first textbook to look to for details on what libsvm and SVMlight are actually doing with your data.
If you have some classified data - a bunch of sample problems paired with their correct answers -, start by training some simple algorithms like K-Nearest-Neighbor and Perceptron and seeing if anything meaningful comes out of it. Don't bother trying to solve it optimally until you know if you can solve it simply or at all.
If you don't have any classified data, or not very much of it, start researching unsupervised learning algorithms.
It sounds like any kind of classifier should work for this problem: find the best class (your dependent variable) for an instance (your events). A simple starting point might be Naive Bayes classification.
This is definitely a machine learning problem. Weka is an excellent choice if you know Java and want a nice GPL lib where all you have to do is select the classifier and write some glue. R is probably not going to cut it for that many instances (events, as you termed it) because it's pretty slow. Furthermore, in R you still need to find or write machine learning libs, though this should be easy given that it's a statistical language.
If you believe that your features (independent variables) are conditionally independent (meaning, independent given the dependent variable), naive Bayes is the perfect classifier, as it is fast, interpretable, accurate and easy to implement. However, with 100,000 instances and only 30-50 features you can likely implement a fairly complex classification scheme that captures a lot of the dependency structure in your data. Your best bet would probably be a support vector machine (SMO in Weka) or a random forest (Yes, it's a silly name, but it helped random forest catch on.) If you want the advantage of easy interpretability of your classifier even at the expense of some accuracy, maybe a straight up J48 decision tree would work. I'd recommend against neural nets, as they're really slow and don't usually work any better in practice than SVMs and random forest.
The book Programming Collective Intelligence has a worked example with source code of a price predictor for laptops which would probably be a good starting point for you.
SVM's are often the best classifier available. It all depends on your problem and your data. For some problems other machine learning algorithms might be better. I have seen problems that neural networks (specifically recurrent neural networks) were better at solving. There is no right answer to this question since it is highly situationally dependent but I agree with dsimcha and Jay that SVM's are the right place to start.
I believe your problem is a regression problem, not a classification problem. The main difference: In classification we are trying to learn the value of a discrete variable, while in regression we are trying to learn the value of a continuous one. The techniques involved may be similar, but the details are different. Linear Regression is what most people try first. There are lots of other regression techniques, if linear regression doesn't do the trick.
You mentioned that you have 30-50 independent variables, and some are more important that the rest. So, assuming that you have historical data (or what we called a training set), you can use PCA (Principal Componenta Analysis) or other dimensionality reduction methods to reduce the number of independent variables. This step is of course optional. Depending on situations, you may get better results by keeping every variables, but add a weight to each one of them based on relevant they are. Here, PCA can help you to compute how "relevant" the variable is.
You also mentioned that events that are occured more recently should be more important. If that's the case, you can weight the recent event higher and the older event lower. Note that the importance of the event doesn't have to grow linearly accoding to time. It may makes more sense if it grow exponentially, so you can play with the numbers here. Or, if you are not lacking of training data, perhaps you can considered dropping off data that are too old.
Like Yuval F said, this does look more like a regression problem rather than a classification problem. Therefore, you can try SVR (Support Vector Regression), which is regression version of SVM (Support Vector Machine).
some other stuff you can try are:
Play around with how you scale the value range of your independent variables. Say, usually [-1...1] or [0...1]. But you can try other ranges to see if they help. Sometimes they do. Most of the time they don't.
If you suspect that there are "hidden" feature vector with a lower dimension, say N << 30 and it's non-linear in nature, you will need non-linear dimensionality reduction. You can read up on kernel PCA or more recently, manifold sculpting.
What you described is a classic classification problem. And in my opinion, why code fresh algorithms at all when you have a tool like Weka around. If I were you, I would run through a list of supervised learning algorithms (I don't completely understand whey people are suggesting unsupervised learning first when this is so clearly a classification problem) using 10-fold (or k-fold) cross validation, which is the default in Weka if I remember, and see what results you get! I would try:
-Neural Nets
-SVMs
-Decision Trees (this one worked really well for me when I was doing a similar problem)
-Boosting with Decision trees/stumps
-Anything else!
Weka makes things so easy and you really can get some useful information. I just took a machine learning class and I did exactly what you're trying to do with the algorithms above, so I know where you're at. For me the boosting with decision stumps worked amazingly well. (BTW, boosting is actually a meta-algorithm and can be applied to most supervised learning algs to usually enhance their results.)
A nice thing aobut using Decision Trees (if you use the ID3 or similar variety) is that it chooses the attributes to split on in order of how well they differientiate the data - in other words, which attributes determine the classification the quickest basically. So you can check out the tree after running the algorithm and see what attribute of a comic book most strongly determines the price - it should be the root of the tree.
Edit: I think Yuval is right, I wasn't paying attention to the problem of discretizing your price value for the classification. However, I don't know if regression is available in Weka, and you can still pretty easily apply classification techniques to this problem. You need to make classes of price values, as in, a number of ranges of prices for the comics, so that you can have a discrete number (like 1 through 10) that represents the price of the comic. Then you can easily run classification it.