Investigations on nature inspired computation based machine learning approaches for categorization of real world engineering applications

Abstract

Advancements in technology produce huge amounts of data in newlinevarious fields increasing the need for efficient and effective data mining tools newlineto uncover the information contained implicitly in the data Such a newlinevoluminous store of data of diverse characteristics is mostly stored and made newlineavailable in digitized form With such a phenomenal increase in the storage newlineand availability of data it is imperative that the available data is wellorganized newlineto generate useful knowledge Data mining methodologies were newlineemployed to interpret the voluminous data at a faster pace and to classify the newlinesame with greater reliability Machine learning techniques have been widely newlineapplied to accurately predict the target class for each case in the data In this newlinethesis learning approaches are proposed integrating several machine learning newlinealgorithms with optimization techniques in a synergistic way to maximize the newlineeffectiveness of a learning task The machine learning techniques like newlineDecision Trees Neural Networks Support Vector Machines and optimization newlinealgorithms namely Biogeography Based Optimization Particle Swarm newlineOptimization and Artificial Bee Colony Optimization were used for classification newlineand transformation of data into useful information newline newline