GLOBAL OPTIMIZATION ENSEMBLE MODEL FOR CLASSIFICATION METHODS FINAL THESIS REPORT

Abstract

Data mining is the process of knowledge discovery and extraction of useful information and pattern from raw data gathered from various resources and supervised learning is the process of data mining for deducing rule from marked training dataset. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. For supervised learning problems there is still no single algorithm that works ideally. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem like bias-variance tradeoff, dimensionality of input space and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. Neither is there any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. The objective of this paper is to create a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm and dataset.