CLIAMS:1. A computer-implemented method for differential evolution-based feature selection, the method comprising:
obtaining a plurality of features extracted from data sets associated with objects representing multiple classes;
computing, by a computing system, an intra-class variation factor and an inter-class variation factor for multiple feature subsets, from amongst the plurality of features; and
identifying, by the computing system, an optimum feature subset, from amongst the multiple feature subsets, based on minimization of the intra-class variation factor and maximization of the inter-class variation factor using differential evolution.
2. The method as claimed in claim 1 further comprising formulating a fitness function based on the intra-class variation factor, the inter-class variation factor, and a Lagrange’s multiplier.
3. The method as claimed in claim 2 further comprising formulating, by the computing system, a population set comprising parameter vectors for the differential evolution, wherein each of the parameter vectors has:
a binary encoded decimal pattern corresponding to a feature subset, from amongst the multiple feature subsets, and
a Lagrange’s multiplier obtained from a range determined by a ratio of an inter-class variation factor and an intra-class variation factor of each of the features.
4. The method as claimed in claim 3, wherein the binary encoded decimal pattern is initially generated randomly.
5. The method as claimed in claim 3, wherein the binary encoded decimal pattern is initially generated uniformly randomly.
6. The method as claimed in claim 1, wherein the identifying of the optimum feature subset is based on the feature subset for which the corresponding fitness function has a minimum value.
7. The method as claimed in claim 1 further comprising classifying, by the computing system, the objects based on a classifier using the optimum feature subset, wherein the classifier is a learning algorithm comprising a support vector machine, a naïve bayes, a decision tree, linear discriminate analysis and a neural network.
8. The method as claimed in claim 1, wherein, for classification of individuals, the data sets are three-dimensional coordinates of skeleton points of each of the individuals, wherein the three-dimensional coordinates of skeleton points are obtained by a skeleton recording device; the plurality of features are gait features of the each of the individuals; and the each of the individuals is an object classified under a distinct class, from amongst the multiple classes.
9. The method as claimed in claim 1, wherein, for cognition load determination of individuals, the data sets are EEG signals obtained from an EEG acquisition device for each of the individuals; the plurality of features is Electroencephalography (EEG) features of the each of the individuals; and cognition load of the each of the individuals is classified under one of the multiple classes.
10. A system (102) for differential evolution-based feature selection, the system (102) comprising:
a processor (104);
a differential evolution feature selection (DEFS) module (114) coupled to the processor (104), to
obtain a plurality of features extracted from data sets associated with objects representing multiple classes;
compute an intra-class variation factor and an inter-class variation factor for multiple feature subsets, from amongst the plurality of features; and
identify an optimum feature subset, from amongst the multiple feature subsets, based on minimization of the intra-class variation factor and maximization of the inter-class variation factor using differential evolution.
11. The system (102) as claimed in claim 10, wherein the DEFS module (114) formulates a fitness function based on the intra-class variation factor, the inter-class variation factor, and a Lagrange’s multiplier.
12. The system (102) as claimed in claim 11, wherein the DEFS module (114) formulates a population set comprising parameter vectors for the differential evolution, wherein each of the parameter vectors has:
a binary encoded decimal pattern corresponding to a feature subset, from amongst the multiple feature subsets, and
a Lagrange’s multiplier obtained from a range determined by a ratio of an inter-class variation factor and an intra-class variation factor of each of the features.
13. The system (102) as claimed in claim 12, wherein the binary encoded decimal pattern is initially generated randomly.
14. The system (102) as claimed in claim 12, wherein the binary encoded decimal pattern is initially generated uniformly randomly.
15. The system (102) as claimed in claim 11, wherein the DEFS module (114) minimizes the fitness function for identifying the optimum feature subset.
16. A non-transitory computer readable medium having a set of computer readable instructions that, when executed, cause a computing system to:
obtain a plurality of features extracted from data sets associated with objects representing multiple classes;
compute an intra-class variation factor and an inter-class variation factor for multiple feature subsets, from amongst the plurality of features; and
identify an optimum feature subset, from amongst the multiple feature subsets, based on minimization of the intra-class variation factor and maximization of the inter-class variation factor using differential evolution. ,TagSPECI:As Attached