Pattern Matching based Classification using Ant Colony Optimization based Feature Selection

Abstract

Classification is a method of accurately predicting the target class for an unlabelled sample by learning from instances described by a set of attributes and a class label. Instance based classifiers are attractive due to their simplicity and performance. However, many of these are susceptible to noise and become unsuitable for real world problems. This paper proposes a novel instance based classification algorithm called Pattern Matching based Classification (PMC). The underlying principle of PMC is that it classifies unlabelled samples by matching for patterns in the training dataset. The advantage of PMC in comparison with other instance based methods is its simple classification procedure together with high performance. To improve the classification accuracy of PMC, an Ant Colony Optimization based Feature Selection algorithm based on the idea of PMC has been proposed. The classifier is evaluated on 35 datasets. Experimental results demonstrate that PMC is competent with many instance based classifiers. The results are also validated using nonparametric statistical tests. Also, the evaluation time of PMC is less when compared to the gravitation based methods used for classification.