Rule Learning and Swarm Intelligence Techniques for Feature Selection Optimization

In mathematics and computer science, solving an optimization problem is to find the best solution from all possible outcomes. In this dissertation work, two kinds of algorithms are considered to address the problems in Microarray Analysis, Numerical Optimization and Wireless Sensor Networks. In gene expression analysis and classification, feature selection is an important process of selecting the optimal subset of relevant features or useful data for further study and prediction. The main objective of feature selection is challenging due to the large search space, computational time, imbalanced samples, and quality of the selected drivers. It is necessary to construct a discriminative and stable feature selector that is robust to noises and outliers and able to select highly informative gene sets