Simultaneous Feature Selection and Weighting – An Evolutionary Multi-objective Optimization Approach

An algorithm for feature selection and weighting to enhance classifier performance.

This is an implementation of the algorithm proposed in [1]. It presents a new feature selection and weighting method aided with the decomposition based evolutionary multi-objective algorithm called MOEA/D. The feature vectors are selected and weighted or scaled simultaneously to project the data points to such a hyper space, here the distance between data points of non-identical classes is increased, thus, making them easier to classify. The inter-class and intra-class distances are simultaneously optimized by using MOEA/D to obtain the optimal features and the scaling factor associated with them. Finally, k-NN (k-Nearest Neighbor) is used to classify the data points having the reduced and weighted feature set.
[1] Sujoy Paul and Swagatam Das. "Simultaneous feature selection and weighting–An evolutionary multi-objective optimization approach." Pattern Recognition Letters 65 (2015): 51-59

Please take a look at the README.txt file for more details and execute the DEMO.m script for a demonstration of the feature selection algorithm.