Open AccessDimensionality Reduction Algorithms on High Dimensional Datasets
Author(s) -
Iwan Syarif
Publication year - 2014
Publication title -
emitter international journal of engineering technology
Language(s) - English
Resource type - Journals
eISSN - 2443-1168
pISSN - 2355-391X
DOI - 10.24003/emitter.v2i2.24
Subject(s) - particle swarm optimization , dimensionality reduction , feature selection , curse of dimensionality , reduction (mathematics) , computer science , genetic algorithm , selection (genetic algorithm) , algorithm , pattern recognition (psychology) , feature (linguistics) , artificial intelligence , data mining , machine learning , mathematics , linguistics , philosophy , geometry
Classification problem especially for high dimensional datasets have attracted many researchers in order to find efficient approaches to address them. However, the classification problem has become very complicatedespecially when the number of possible different combinations of variables is so high. In this research, we evaluate the performance of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as feature selection algorithms when applied to high dimensional datasets.Our experiments show that in terms of dimensionality reduction, PSO is much better than GA. PSO has successfully reduced the number of attributes of 8 datasets to 13.47% on average while GA is only 31.36% on average. In terms of classification performance, GA is slightly better than PSO. GA‐ reduced datasets have better performance than their original ones on 5 of 8 datasets while PSO is only 3 of 8 datasets.Keywords: feature selection, dimensionality reduction, Genetic Algorithm (GA), Particle Swarm Optmization (PSO).