I have a question about some basic concepts of machine learning. The examples, I observed, were giving a brief overview .For training the system, feature vector is given as input. In case of supervised learning, the dataset is labelled. I have confusion about labelling. For example if I have to distinguish between two types of pictures, I will provide a feature vector and on output side for testing, I'll provide 1 for type A and 2 for type B. But if I want to extract a region of interest from a dataset of images. How will I label my data to extract ROI using SVM. I hope I am able to convey my confusion. Thanks in anticipation.
In supervised learning, such as SVMs, the dataset should be composed as follows:
<i-th feature vector><i-th label>
where i goes from 1 to the number of patterns (also examples or observations) in your training set so this represents a single record in your training set which can be used to train the SVM classifier.
So you basically have a set composed by such tuples and if you do have just 2 labels (binary classification problem) you can easily use a SVM. Indeed the SVM model will be trained thanks to the training set and the training labels and once the training phase has finished you can use another set (called Validation Set or Test Set), which is structured in the same way as the training set, to test the accuracy of your SVMs.
In other words the SVM workflow should be structured as follows:
train the SVM using the training set and the training labels
predict the labels for the validation set using the model trained in the previous step
if you know what the actual validation labels are, you can match the predicted labels with the actual labels and check how many labels have been correctly predicted. The ratio between the number of correctly predicted labels and the total number of labels in the validation set returns a scalar between [0;1] and it's called the accuracy of your SVM model.
if you're interested in the ROI, you might want to check the trained SVM parameters (mainly the weights and bias) to reconstruct the separation hyperplane
It is also important to know that the training set records should be correctly, a priori labelled: if the training labels are not correct, the SVM will never be able to correctly predict the output for previously unseen patterns. You do not have to label your data according to the ROI you want to extract, the data must be correctly labelled a priori: the SVM will have the entire set of type A pictures and the set of type B pictures and will learn the decision boundary to separate pictures of type A and pictures of type B. You do not have to trick the labels: if you do, you're not doing classification and/or machine learning and/or pattern recognition. You're basically tricking the results.
Related
I am completely new to Machine Learning algorithms and I have a quick question with respect to Classification of a dataset.
Currently there is a training data that consists of two columns Message and Identifier.
Message - Typical message extracted from Log containing timestamp and some text
Identifier - Should classify the category based on the message content.
The training data was prepared by extracting a particular category from the tool and labelling it accordingly.
Now the test data contains just the message and I am trying to obtain the Category accordingly.
Which approach is most helpful in this scenario ? Is it the Supervised or Unsupervised Learning ?
I have a trained dataset and I am trying to predict the Category for the Test Data.
Thanks in advance,
Adam
If your labels are exact then you can classify using ANN, SVM etc. But labels are not exact you have to cluster data with respect to the features you have in data. K-means or nearest neighbour can be the starting point for clustering.
It is supervised learning, and a classification problem.
However, obviously you do not have the label column (the to-be-predicted value) for your testset. Thus, you cannot calculate error measures (such as False Positive Rate, Accuracy etc) for that test set.
You could, however, split the set of labeled training data that you do have into a smaller training set and a validation set. Split it 70%/30%, perhaps. Then build a prediction model from your smaller 70% training dataset. Then tune it on your 30% validation set. When accuracy is good enough, then apply it on your testset to obtain/predict the missing values.
Which techniques / algorithms to use is a different question. You do not give enough information to answer that. And even if you did you still need to tune the model yourself.
You have labels to predict, and training data.
So by definition it is a supervised problem.
Try any classifier for text, such as NB, kNN, SVM, ANN, RF, ...
It's hard to predict which will work best on your data. You willhave to try and evaluate several.
I have to solve 2 class classification problem.
I have 2 classifiers that output probabilities. Both of them are neural networks of different architecture.
Those 2 classifiers are trained and saved into 2 files.
Now I want to build meta classifier that will take probabilities as input and learn weights of those 2 classifiers.
So it will automatically decide how much should I "trust" each of my classifiers.
This model is described here:
http://rasbt.github.io/mlxtend/user_guide/classifier/StackingClassifier/#stackingclassifier
I plan to use mlxtend library, but it seems that StackingClassifier refits models.
I do not want to refit because it takes very huge amount of time.
From the other side I understand that refitting is necessary to "coordinate" work of each classifier and "tune" the whole system.
What should I do in such situation?
I won't talk about mlxtend because I haven't worked with it but I'll tell you the general idea.
You don't have to refit these models to the training set but you have to refit them to parts of it so you can create out-of-fold predictions.
Specifically, split your training data in a few pieces (usually 3 to 10). Keep one piece (i.e. fold) as validation data and train both models on the other folds. Then, predict the probabilities for the validation data using both models. Repeat the procedure treating each fold as a validation set. In the end, you should have the probabilities for all data points in the training set.
Then, you can train a meta-classifier using these probabilities and the ground truth labels. You can use the trained meta-classifier on your new data.
I'm implementing color quantization based on k-means clustering method on some RGB images. Then, I will determine the performance the algorithm. I found some information about training and testing. As I understand, I should divide the samples of images for training and testing.
But I am confused about the terms training and testing. What does these mean ? And how to implement with a rank value ?
Training and testing are two common concepts in machine learning. Training and testing are more easily explained in the framework of supervised learning; where you have a training dataset for which you know both input data as well as additional attributes that you want to predict. Training consists in learning a relation between data and attributes from a fraction of the training dataset, and testing consists in testing predictions of this relation on another part of the dataset (since you know the prediction, you can compare the output of the relation and the real attributes). A good introductory tutorial using these concepts can be found on http://scikit-learn.org/stable/tutorial/basic/tutorial.html
However, clustering is a class of unsupervised learning, that is, you just have some input data (here, the RGB values of pixels, if I understand well), without any corresponding target values. Therefore, you can run a k-means clustering algorithm in order to find classes of pixels with similar colors, without the need to train and test the algorithm.
In image processing, training and testing is for example used for classifying pixels in order to segment different objects. A common example is to use a random forest classifier: the user selects pixels belonging to the different objects of interest (eg background and object), the classifier is trained on this set of pixels, and then the remainder of the pixels are attributed to one of the classes by the classifier. ilastik (http://ilastik.org/) is an example of software that performs interactive image classification and segmentation.
I don't know which programming language you're using, but k-means is already implemented in various libraries. For Python, both SciPy (http://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.vq.kmeans2.html#scipy.cluster.vq.kmeans2) and scikit-learn (http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) have an implementation of K-means. Also note that, depending on your application, you may be interested in clustering pixels together using not only pixels values, but also spatial proximity of pixels. See for example the scikit-image gallery example http://scikit-image.org/docs/dev/auto_examples/plot_rag_mean_color.html
I'm trying to solve a text classification problem for academic purpose. I need to classify the tweets into labels like "cloud" ,"cold", "dry", "hot", "humid", "hurricane", "ice", "rain", "snow", "storms", "wind" and "other". Each tweet in training data has probabilities against all the label. Say the message "Can already tell it's going to be a tough scoring day. It's as windy right now as it was yesterday afternoon." has 21% chance for being hot and 79% chance for wind. I have worked on the classification problems which predicts whether its wind or hot or others. But in this problem, each training data has probabilities against all the labels. I have previously used mahout naive bayes classifier which take a specific label for a given text to build model. How to convert these input probabilities for various labels as input to any classifier?
In a probabilistic setting, these probabilities reflect uncertainty about the class label of your training instance. This affects parameter learning in your classifier.
There's a natural way to incorporate this: in Naive Bayes, for instance, when estimating parameters in your models, instead of each word getting a count of one for the class to which the document belongs, it gets a count of probability. Thus documents with high probability of belonging to a class contribute more to that class's parameters. The situation is exactly equivalent to when learning a mixture of multinomials model using EM, where the probabilities you have are identical to the membership/indicator variables for your instances.
Alternatively, if your classifier were a neural net with softmax output, instead of the target output being a vector with a single [1] and lots of zeros, the target output becomes the probability vector you're supplied with.
I don't, unfortunately, know of any standard implementations that would allow you to incorporate these ideas.
If you want an off the shelf solution, you could use a learner the supports multiclass classification and instance weights. Let's say you have k classes with probabilities p_1, ..., p_k. For each input instance, create k new training instances with identical features, and with label 1, ..., k, and assign weights p_1, ..., p_k respectively.
Vowpal Wabbit is one such learner that supports multiclass classification with instance weights.
The following is adaboost algorithm:
It mentions "using weights wi on the training data" at part 3.1.
I am not very clear about how to use the weights. Should I resample the training data?
I am not very clear about how to use the weights. Should I resample the training data?
It depends on what classifier you are using.
If your classifier can take instance weight (weighted training examples) into account, then you don't need to resample the data. An example classifier could be naive bayes classifier that accumulates weighted counts or a weighted k-nearest-neighbor classifier.
Otherwise, you want to resample the data using the instance weight, i.e., those instance with more weights could be sampled multiple times; while those instance with little weight might not even appear in the training data. Most of the other classifiers fall in this category.
In Practice
Actually in practice, boosting performs better if you only rely on a pool of very naive classifiers, e.g., decision stump, linear discriminant. In this case, the algorithm you listed has a easy-to-implement form (see here for details):
Where alpha is chosen by (epsilon is defined similarly as yours).
An Example
Define a two-class problem in the plane (for example, a circle of points
inside a square) and build a strong classier out of a pool of randomly
generated linear discriminants of the type sign(ax1 + bx2 + c).
The two class labels are represented with red crosses and blue dots. We here are using a bunch of linear discriminants (yellow lines) to construct the pool of naive/weak classifiers. We generate 1000 data points for each class in the graph (inside the circle or not) and 20% of data is reserved for testing.
This is the classification result (in the test dataset) I got, in which I used 50 linear discriminants. The training error is 1.45% and the testing error is 2.3%
The weights are the values applied to each example (sample) in step 2. These weights are then updated at step 3.3 (wi).
So initially all weights are equal (step 2) and they are increased for wrongly classified data and decreased for correctly classified data. So in step 3.1 you have to take take these value in account to determine a new classifier, giving more importance to higher weight values. If you did not change the weight you would produce exactly the same classifier each time you execute step 3.1.
These weights are only used for training purpose, they're not part of the final model.