this question is about LibSVM or SVMs in general.
I wonder if it is possible to categorize Feature-Vectors of different length with the same SVM Model.
Let's say we train the SVM with about 1000 Instances of the following Feature Vector:
[feature1 feature2 feature3 feature4 feature5]
Now I want to predict a test-vector which has the same length of 5.
If the probability I receive is to poor, I now want to check the first subset of my test-vector containing the columns 2-5. So I want to dismiss the 1 feature.
My question now is: Is it possible to tell the SVM only to check the features 2-5 for prediction (e.g. with weights), or do I have to train different SVM Models. One for 5 features, another for 4 features and so on...?
Thanks in advance...
marcus
You can always remove features from your test points by fiddling with the file, but I highly recommend not using such an approach. An SVM model is valid when all features are present. If you are using the linear kernel, simply setting a given feature to 0 will implicitly cause it to be ignored (though you should not do this). When using other kernels, this is very much a no no.
Using a different set of features for predictions than the set you used for training is not a good approach.
I strongly suggest to train a new model for the subset of features you wish to use in prediction.
Related
Would it be accurate to include an expert system in an image classifying application? (I am working with Matlab, have some experience with image processing and no experience with expert systems.)
What I'm planning on doing is adding an extra feature vector that is actually an answer to a question. Is this fine?
For example: Assume I have two questions that I want the answers to : Question 1 and Question 2. Knowing the answers to these 2 questions should help classify the test image more accurately. I understand expert systems are coded differently from an image classifier but my question is would it be wrong to include the answers to these 2 questions, in a numerical form (1 can be yes, and 0 can be no) and pass this information along with the other feature vectors into a classifier.
If it matters, my current classifier is an SVM.
Regarding training images: yes, they too will be trained with the 2 extra feature vectors.
Converting a set of comments to an answer:
A similar question in cross-validated already explains that it can be done as long as data is properly preprocessed.
In short: you can combine them as long as training (and testing) data is properly preprocessed (e.g. standardized). Standardization improves the performance of most linear classifiers because it scales the variables so they have the similar weight in the learning process and improves the numerical stability (and performance) when variables are sampled from gaussian-like distributions (which is achieved by standarization).
With that, if continuous variables are standardized and categorical variables are encoded as (-1, +1) the SVM should work well. Whether it will improve or not the performance of the classifier depends on the quality of those cathegorical variables.
Answering the other question in the comment.. while using kernel SVM with for example a chi square kernel, the rows of the training data are suppose to behave like histograms (all positive and usually l1-normalized) and therefore introducing a (-1, +1) feature breaks the kernel. Using a RBF kernel the rows of the data are suppose to be L2 normalized, and again, introducing (-1, +1) features might introduce unexpected behaviour (I'm not very sure what exactly the effect would be..).
I worked on similar problem. if multiple features can be extracted from your images then you can train different classifier by using different features. You can think about these classifiers as experts in answering questions based on the features they used in training. Instead of using labels as outputs, it is better to use confidence values. uncertainty can be very important in this manner. you can use these experts to generate values. these values can be combined and used to train another classifier.
I am trying to detect the faces using the Matlab built-in viola jones face detection. Is there anyway that I can combine two classification models like "FrontalFaceCART" and "ProfileFace" into one in order to get a better result?
Thank you.
You can't combine models. That's a non-sense in any classification task since every classifier is different (works differently, i.e. different algorithm behind it, and maybe is also trained differently).
According to the classification model(s) help (which can be found here), your two classifiers work as follows:
FrontalFaceCART is a model composed of weak classifiers, based on classification and regression tree analysis
ProfileFace is composed of weak classifiers, based on a decision stump
More infos can be found in the link provided but you can easily see that their inner behaviour is rather different, so you can't mix them or combine them.
It's like (in Machine Learning) mixing a Support Vector Machine with a K-Nearest Neighbour: the first one uses separating hyperplanes whereas the latter is simply based on distance(s).
You can, however, train several models in parallel (e.g. independently) and choose the model that better suits you (e.g. smaller error rate/higher accuracy): so you basically create as many different classifiers as you like, give them the same training set, evaluate each accuracy (and/or other parameters) and choose the best model.
One option is to make a hierarchical classifier. So in a first step you use the frontal face classifier (assuming that most pictures are frontal faces). If the classifier fails, you try with the profile classifier.
I did that with a dataset of faces and it improved my overall classification accuracy. Furthermore, if you have some a priori information, you can use it. In my case the faces were usually in the middle up part of the picture.
To further improve your performance, without using the two classifiers in MATLAB you are using, you would need to change your technique (and probably your programming language). This is the best method so far: Facenet.
I need to classify pairs of image and indicate whether they're the same of not. I use several descriptors as SIFT LBP and more.
I want now to use LIBSVM to do the training and test.
how can I use teh svmTrain.
should I save only the distance between 2 descriptors and then just have 1 1:SIftDelta, 2:LBPDelta
is this the correct way or is there any better approach?
thanks
I'm not sure this is the right forum for this question, as it deals more with "high level" notions of learning, rather the specific implementation of it in Matlab.
Having said that, it seems like you are trying to combine multiple cues for learning, which is not a trivial task.
I can propose two methods for you:
Direct method - just concatenate all your descriptors into a single, very long, one and do the learning in this high dimensional space.
Do the learning in two stages (consequently, you'll have to partition your training data into two):
At the first stage, learn K classifiers, each using a different descriptor (assuming you wish to use K different descriptors).
Then, at the second stage, (using the reminder of your training data), you classify each example using the K classifiers you have: this will give you a new K-dimensional feature vector for each sample (you can put the classification result, or use the distance from the separating hyper plane to populate the k-th entry in the new descriptor). Now you can train a second classifier on the new K-dimension vectors. This second classifier gives you the final output of your multi-descriptor system.
-Enjoy!
I have already applied SVM using LIBSVM. Now i would like to implement Genetic Algorithm for feature selection. Tried to google for some information
1) Saw this website : http://www.scribd.com/doc/31235552/Genetic-Algorithm-Implementation-Using-Matlab
2) GA Examples in MATLAB : http://www.mathworks.com/help/toolbox/gads/f6691.html
Have few questions on them
Q1) [x fval] = ga(#fitnessfun, nvars, options). This is the function to do gasolver. What should be the fitnessfun? In most ga, it is a polynomial function. But in the case of SVM, what shld be the fitnessfun?
Q2) is there any concrete examples for GA after SVM?
Like to hear some feedback.
Thanks in advance.
If you want to do feature selection, I think you have it backwards. You should run the GA for feature selection before the training of your SVM. Your fitness function could become the performance of a newly trained SVM on selected features, it depends on what you want to accomplish. Can't say you were very clear on this topic.
To answer your second comment:
There are many parts, I don't know this ga function you are using, but if you take a look at the documentation they must tell you somewhere what parameters this fitnessfun should be expecting. I'm guessing the individual for which you want to evaluate fitness is the main parameter for this function. If you evolve a selection of features, this individual would be an array of Boolean variables where true indicates a feature that is selected an false indicates a feature that is not selected. This fitness function needs to return an indicator of how well this selection of features fares, i.e. it must return a higher number for a better selection, and a lower number for a worst selection. Prediction accuracy might be a good value for this (nb. of correct predictions divided by the total number of samples).
I'm going to assume you know how to calculate the prediction accuracy of an SVM model given a dataset and its labels. Since you have a pre-trained SVM it might be a bit tricky to use it only for selected features, and it depends strongly upon the implementation of your SVM. If it is a linear SVM, you could just set the values of the non-selected features to zero in the data matrix. However, if it is an RBF SVM that won't work. You will need to understand the inner mechanisms of the SVM implementation you are relying on. I suggest making a simple example where you train an SVM on 3d data and then adapt it to work on 2d data. It strongly depends on the implementation of your SVM model.
I'm trying to build an app to detect images which are advertisements from the webpages. Once I detect those I`ll not be allowing those to be displayed on the client side.
Basically I'm using Back-propagation algorithm to train the neural network using the dataset given here: http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements.
But in that dataset no. of attributes are very high. In fact one of the mentors of the project told me that If you train the Neural Network with that many attributes, it'll take lots of time to get trained. So is there a way to optimize the input dataset? Or I just have to use that many attributes?
1558 is actually a modest number of features/attributes. The # of instances(3279) is also small. The problem is not on the dataset side, but on the training algorithm side.
ANN is slow in training, I'd suggest you to use a logistic regression or svm. Both of them are very fast to train. Especially, svm has a lot of fast algorithms.
In this dataset, you are actually analyzing text, but not image. I think a linear family classifier, i.e. logistic regression or svm, is better for your job.
If you are using for production and you cannot use open source code. Logistic regression is very easy to implement compared to a good ANN and SVM.
If you decide to use logistic regression or SVM, I can future recommend some articles or source code for you to refer.
If you're actually using a backpropagation network with 1558 input nodes and only 3279 samples, then the training time is the least of your problems: Even if you have a very small network with only one hidden layer containing 10 neurons, you have 1558*10 weights between the input layer and the hidden layer. How can you expect to get a good estimate for 15580 degrees of freedom from only 3279 samples? (And that simple calculation doesn't even take the "curse of dimensionality" into account)
You have to analyze your data to find out how to optimize it. Try to understand your input data: Which (tuples of) features are (jointly) statistically significant? (use standard statistical methods for this) Are some features redundant? (Principal component analysis is a good stating point for this.) Don't expect the artificial neural network to do that work for you.
Also: remeber Duda&Hart's famous "no-free-lunch-theorem": No classification algorithm works for every problem. And for any classification algorithm X, there is a problem where flipping a coin leads to better results than X. If you take this into account, deciding what algorithm to use before analyzing your data might not be a smart idea. You might well have picked the algorithm that actually performs worse than blind guessing on your specific problem! (By the way: Duda&Hart&Storks's book about pattern classification is a great starting point to learn about this, if you haven't read it yet.)
aplly a seperate ANN for each category of features
for example
457 inputs 1 output for url terms ( ANN1 )
495 inputs 1 output for origurl ( ANN2 )
...
then train all of them
use another main ANN to join results