I am working on a machine learning project and my data is imbalanced, with one class having a much smaller number of examples than the other. This has led to biased models and poor predictive performance, especially for my target variable which is of high importance. I have already tried resampling techniques such as oversampling, undersampling, and both, but I'm still struggling to achieve the desired performance.
One possible solution from paulduf for handling class imbalance is to use a package that is specifically designed for this purpose. They mentioned using the 'imbalanced-learn' package, which is compatible with scikit-learn, has a lot of stars and looks up-to-date/maintained. I looked at the package and it does provide a variety of methods for dealing with imbalanced datasets, such as sampling techniques, cost-sensitive learning, and ensemble methods. Here's the link to the package: github.com/scikit-learn-contrib/imbalanced-learn imbalanced-learn.org/stable/user_guide.html Thank you paulduf.
Aside from using a package, what are some other effective tips and best practices for handling class imbalance in machine learning that have been shown to work in real-world scenarios? Are there any specific methods that have been proven to be successful in addressing this problem, such as anomaly detection techniques? Any help or guidance would be greatly appreciated, as I am looking to improve the performance of my machine learning models in the context of imbalanced datasets.
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I have been working on the case study where data is highly imbalanced. we have been taught we can handle the imbalanced data by either under sampling the majority class or over sampling the minority class.
I wanted to ask if there is any other way/method that can be used to handle imbalanced data?
this question is more on conceptual side than programming.
for example,
I was thinking if we could put some weight on minority class (conceptually) to make the model emphasize on identifying pattern in minority class.
I don't know how that can be done but this concept theoretically should work.
feel free to put crazy ideas too.
Your weights idea is not far off. This can be done. In fact, most sklearn Models give you the option to specify class weights. This however is often not enough for very extreme cases (e.g. 95%/5% split or more extreme).
There are specific oversampling techniques such as SMOTE (and related techniques) which go a step further than classic oversampling and generate synthetic samples based on a K Nearest Neighbors Algorithm.
If the classes are extremely imbalanced a "classic" classification approach may not be enough and you might have to look into anomaly detection algorithms.
Just use strong consistent classifier. See https://arxiv.org/abs/2201.08528
Generally speaking I think that you need, before diving into the technical solution (under/upsampling, smote ..) consider the business KPI you are predicting and whether there is a proxy that could help reduce the disparity rate between the classes.
You can think also about the models that have weights parameters and could penalize the majority class
You can check this article, it explains from a conceptual point of view how to deal with imbalanced data in general.
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 am trying to implement an SVM in Rapidminer. However I am presented with several SVM implementations, libsvm, mysvm,JMySVM, Particle Swarm Optimization based SVM and Evolutionary SVM. Know I know the basic differences between the implementations but what are the advantages and disadvantages of them to know which one to implement?
I am not finding much information about this online, I would like to avoid a try them all to see which one presents the best results. So I would like to know in which situation I should use them.
From the first, you seem to confuse different implementations and algorithms. As far as I know, libsvm, mysvm and JmySVM are standard implementation which solve the SVM optimization problem by algorithms such as sequential minimal optimization.
On the contrary, the other SVMs you mentioned (additionally) use less common approaches like particle swarm optimization or evolutionary algorithms for the optimization. Such methods usually give you good approximation with small effort, which might be advantageous for large-scale problems (--but I admit I don't know the exact motivation for their invention).
If you are looking for the SVM model which is common in machine learning and related fields, I would suggest you to try the library libsvm. Alternatively, you can have a look on the collection here.
Many machine learning competitions are held in Kaggle where a training set and a set of features and a test set is given whose output label is to be decided based by utilizing a training set.
It is pretty clear that here supervised learning algorithms like decision tree, SVM etc. are applicable. My question is, how should I start to approach such problems, I mean whether to start with decision tree or SVM or some other algorithm or is there is any other approach i.e. how will I decide?
So, I had never heard of Kaggle until reading your post--thank you so much, it looks awesome. Upon exploring their site, I found a portion that will guide you well. On the competitions page (click all competitions), you see Digit Recognizer and Facial Keypoints Detection, both of which are competitions, but are there for educational purposes, tutorials are provided (tutorial isn't available for the facial keypoints detection yet, as the competition is in its infancy. In addition to the general forums, competitions have forums also, which I imagine is very helpful.
If you're interesting in the mathematical foundations of machine learning, and are relatively new to it, may I suggest Bayesian Reasoning and Machine Learning. It's no cakewalk, but it's much friendlier than its counterparts, without a loss of rigor.
EDIT:
I found the tutorials page on Kaggle, which seems to be a summary of all of their tutorials. Additionally, scikit-learn, a python library, offers a ton of descriptions/explanations of machine learning algorithms.
This cheatsheet http://peekaboo-vision.blogspot.pt/2013/01/machine-learning-cheat-sheet-for-scikit.html is a good starting point. In my experience using several algorithms at the same time can often give better results, eg logistic regression and svm where the results of each one have a predefined weight. And test, test, test ;)
There is No Free Lunch in data mining. You won't know which methods work best until you try lots of them.
That being said, there is also a trade-off between understandability and accuracy in data mining. Decision Trees and KNN tend to be understandable, but less accurate than SVM or Random Forests. Kaggle looks for high accuracy over understandability.
It also depends on the number of attributes. Some learners can handle many attributes, like SVM, whereas others are slow with many attributes, like neural nets.
You can shrink the number of attributes by using PCA, which has helped in several Kaggle competitions.
I'm working on a classification problem where I have data about only One Class, so I wanna classify between that "Target"class against all other possibilities which is the "Outlier" Class in incremental learning. So, I have found some libraries, but none of them support updating classifier.
Do you know any library that supports one-class classifier with updating pre-existed classifier especially in java or matlab?
I can't think of any full pre-existing solution to your question. However, I can suggest two approaches:
Neural networks have been used for various types of anomaly detection (e.g. see here, with the problem framed as "novelty detection"). Depending on the nature of your problem, this might be a suitable solution, as NNs can be incrementally trained and are supported by several widely used libraries. The right one to use would be highly dependent on your problem framing and the network architecture chosen.
Although most SVM libraries do not support incremental training, there are some with such support (e.g. see in Can an SVM learn incrementally?). However, as far as I can see, none of the two libraries suggested in the cited reference supports unary classification. But you could try basing a tailored solution on one of them (their source code seems to be freely available).
PS if you found one of these (or any other) solution to work, please post it as an answer as well :)