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.
