I want to choose the best feature subset available that distinguish two classes to be fed into a statistical framework that I have built, where features are not independent.
After looking at the feature selection methods on machine learning it seems that it fall into three different categories: Filter,wrapper and Embedded methods. and the filter methods can be either: univariate or multivariate. It does make sense to use either Filter(multivariate) or wrapper methods because both -as I understood- looks for the best subset, however, as I am not using a classifier how can use it ?
Does it make sense to apply such methods (e.g. Recursive feature
elimination ) to DT or Random Forest classifier where the features
have rules there, and then take the resulted best subset and fed it
into my framework ?**
Also, as most of the algorithms provided by Scikit-learn are
univariate algorithms, Is there any other python-based libraries
that provide more subset feature selection algorithms ?
I think the statement that "most of the algorithms provided by Scikit-learn are univariate algorithms" is false. Scikit-learn handles multi-dimensional data very nicely. The RandomForestClassifier that they provide will give you an estimate of feature importance.
Another way to estimate feature importance is to choose any classifier that you like, train it and estimate performance on a validation set. Record the accuracy and this will be your baseline. Then take that same train/validation split and randomly permute all values along one feature dimension. Then train and validate again. Record the difference of this accuracy from your baseline. Repeat this for all feature dimensions. The results will be a list of numbers for each feature dimension that indicates its importance.
You can extend this to use pairs, or triples of feature dimensions, but the computational cost will grow quickly. If you're features are highly correlated you may benefit from doing this for at least the pairwise case.
Here is the source document of where I learned that trick: http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#varimp
(It should work for classifiers other than Random Forests.)
Related
Terminology:
Component: PC
loading-score[i,j]: the j feature in PC[i]
Question:
I know the question regarding feature selection is asked several times here at StackOverflow (SO) and on other tech-pages, and it proposes different answers/discussion. That is why I want to open a discussion for the different solutions, and not post it as a general question since that has been done.
Different methods are proposed for feature selection using PCA: For instance using the dot product between the original features and the components (here) to get their correlation, a discussion at SO here suggests that you can only talk about important features as loading-scores in a component (and not use that importance in the input space), and another discussion at SO (which I cannot find at the moment) suggest that the importance for feature[j] would be abs(sum(loading_score[:,j]) i.e the sum of the absolute value of loading_score[i,j] for all i components.
I personally would think that a way to get the importance of a feature would be an absolute sum where each loading_score[i,j] is weighted by the explained variance of component i i.e
imp_feature[j]=sum_i (abs(loading_score[i,j])*explained_variance[i].
Well, there is no universal way to select features; it totally depends on the dataset and some insights available about the dataset. I will provide some examples which might be helpful.
Since you asked about PCA, initially it separates the whole dataset into two sets under which the variances. On the other ICA (Independent Component Analysis) is able to extract multiple features simultaneously. Look at this example,
In this example, we mix three independent signals and try to separate out them using ICA and PCA. In this case, ICA is doing it a better way than PCA. In general, if you search Blind Souce Separation (BSS) you may find more information about this. Besides, in this example, we know the independent components thus, separation is easy. In general, we do not know the number of components. Hence, you may have to guess based on some prior information about the dataset. Also, you may use LDA (Linear Discriminate Analysis) to reduce the number of features.
Once you extract PC components using any of the techniques, following way we can visualize it. If we assume, those extracted components as random variables i.e., x, y, z
More information about you may refer to this original source where I took about two figures.
Coming back to your proposition,
imp_feature[j]=sum_i (abs(loading_score[i,j])*explained_variance[i]
I would not recommend this way due to the following reasons:
abs(loading_score[i,j]) when we get absolute values you may loose positive or negative correlations of considered features. explained_variance[i] may be used to find the correlation between features, but multiplying does not make any sense.
Edit:
In PCA, each component has its explained variance. Explained variance is the ratio between individual component variance and total variance(sum of all individual components variances). Feature significance can be measured by magnitude of explained variance.
All in all, what I want to say, feature selection totally depends on the dataset and the significance of features. PCA is just one technique. Frist understand the properties of features and the dataset. Then, try to extract features. Hope this helps. If you can provide us with an exact example, we may provide more insights.
I work on palmprint recognition using feature2D with Open_CV library, and I use algorithms such as SIFT, SURF, ORB... to detect features and extract/match descriptors. My test include (1 vs 1) palmprint and also (1 vs Data Base) of palmprint.
Ones I get the result, I need to evaluate the algorithm, and for this I know that there are some rates or scores (like EER, rank-1 identification, recall and accuracy) which gives an estimation about how much this method was successful. Now I need to know if any of those rates are implemented in Open_CV, and how to use them. If they aren't, what are the different formulas used in the literary.
As far as I know there is little implemented in OpenCV. A common way is to store the results (e.g. in JSON) and process those with other programs such as Matlab or Python. This also allows you to change the evaluation without the need to recompute the algorithms.
There is no overall best method to show the results. It always depends on what you want to show. In my opinion ROC is the best way to express your output. It is also very widely used in research.
If you insist on doing it in C++, then you could use:
Roceasy or
DLIB
I'm performing an experiment in which I need to compare classification performance of several classification algorithms for spam filtering, viz. Naive Bayes, SVM, J48, k-NN, RandomForests, etc. I'm using the WEKA data mining tool. While going through the literature I came to know about various dimension reduction methods which can be broadly classified into two types-
Feature Reduction: Principal Component Analysis, Latent Semantic Analysis, etc.
Feature Selection: Chi-Square, InfoGain, GainRatio, etc.
I have also read this tutorial of WEKA by Jose Maria in his blog: http://jmgomezhidalgo.blogspot.com.es/2013/02/text-mining-in-weka-revisited-selecting.html
In this blog he writes, "A typical text classification problem in which dimensionality reduction can be a big mistake is spam filtering". So, now I'm confused whether dimensionality reduction is of any use in case of spam filtering or not?
Further, I have also read in the literature about Document Frequency and TF-IDF as being one of feature reduction techniques. But I'm not sure how does it work and come into play during classification.
I know how to use weka, chain filters and classifiers, etc. The problem I'm facing is since I don't have enough idea about feature selection/reduction (including TF-IDF) I am unable to decide how and what feature selection techniques and classification algorithms I should combine to make my study meaningful. I also have no idea about optimal threshold value that I should use with chi-square, info gain, etc.
In StringToWordVector class, I have an option of IDFTransform, so does it makes sence to set it to TRUE and also use a feature selection technique, say InfoGain?
Please guide me and if possible please provide links to resources where I can learn about dimension reduction in detail and can plan my experiment meaningfully!
Well, Naive Bayes seems to work best for spam filtering, and it doesn't play nicely with dimensionality reduction.
Many dimensionality reduction methods try to identify the features of the highest variance. This of course won't help a lot with spam detection, you want discriminative features.
Plus, there is not only one type of spam, but many. Which is likely why naive Bayes works better than many other methods that assume there is only one type of spam.
I am using the Scikit-learn Extremely Randomized Trees algorithm to get info about the relative feature importances and I have a question about how "redundant features" are ranked.
If I have two features that are identical (redundant) and important to the classification, the extremely randomized trees cannot detect the redundancy of the features. That is, both features get a high ranking. Is there any other way to detect that two features are actualy redundant?
Maybe you could extract the top n important features and then compute pairwise Spearman's or Pearson's correlations for those in order to detect redundancy only for the top informative features as it might not be feasible to compute all pairwise feature correlations (quadratic with the number of features).
There might be more clever ways to do the same by exploiting the statistics of the relative occurrences of the features as nodes in the decision trees though.
I want to develop an Intrusion Detection System (IDS) that might be used with one of the KDD datasets. In the present case, my dataset has 42 attributes and more than 4,000,000 rows of data.
I am trying to build my IDS using fuzzy association rules, hence my question: What is actually considered as the best tool for fuzzy logic in this context?
Fuzzy association rule algorithms are often extensions of normal association rule algorithms like Apriori and FP-growth in order to model uncertainty using probability ranges. I thus assume that your data consists of quite uncertain measurements and therefore you want to group the measurements in more general ranges like e.g. 'low'/'medium'/'high'. From there on you can use any normal association rule algorithm to find the rules for your IDS (I'd suggest FP-growth as it has lower complexity than Apriori for large data sets).