Feature Selection Algorithms Performance
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A further question would be whether there are any metrics known that measure the performance of feature selection algorithms.
Feature selection algorithms performance. This can be done as before by training the algorithm on the unmodified data and applying an importance score to each feature. Feature selection fs algorithms abolish inappropriate information from the repositories of educational background so that performance of classifier in terms of accuracy could be increased and the same could be used for better decision. To compare feature selection algorithms we choose various kinds of datasets which contain varying numbers of features and samples.
In the light of this mentioned fact it is necessary to choose a feature selection algorithm carefully. What would be an appropriate method to compare different feature selection algorithms and to select the best method for a given problem dataset. Duke leukemia dlbcl and carcinoma are well known microarray datasets.
In lieu of the above a best feature selection algorithm must be selected. This paper presents an analysis of the performance of filter feature selection algorithms and classification algorithms on two different student datasets. Other datasets come from the uci repository and several websites.
We can also use randomforest to select features based on feature importance. As said before embedded methods use algorithms that have built in feature selection methods. We calculate feature importance using node impurities in each decision tree.
In random forest the final feature importance is the average of all decision tree feature importance. The converse of rfe goes by a variety of names one of which is forward feature selection. The performance metrics for feature selection clustering and classificatio n algorithms.
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