New Approach to Improve Classification Accuracy Using Ant Clony Optimization

The selection of a classifier is only one aspect of the problem of data classification. Equally important (if not, more so) is the pre-processing strategy to be employed. In this paper, a pre-processing step is proposed in order to increase accuracy of classification. The objective of this pre-processing step is to achieve a high degree of separation among classes before the classifier is trained or tested. This results into a trace ratio problem which is difficult to solve. Methods such as Linear Discriminant Analysis (LDA) have already been used for the solution of this problem by turning it into a simpler yet inexact problem. In our approach ACO is used to solve the trace ratio problem directly also can increase classification accuracy by finding a transformation matrix to discriminate between classes.