Instance reduction algorithm based on elitist min-max ant colony optimization technique

Abstract

Although the advancement of technology has made data processing quite easier, it is still an enormous task, especially if the volume is large and can affect the learning capability of the classifier. Instance Reduction techniques can be employed on data to attain best results while making a substantial difference in terms of data storage needs, and processing speed. The proposed novel algorithm – Elitist Min-Max Ant Colony based Instance Reduction (ÆIR), can be attributed to the hybridization of bio-inspired optimization algorithms and Machine Learning algorithms for instance reduction. The primary features of ÆIR are the introduction of new hyperparameters such as the elitist ants, the min-max bounding of pheromones, and the incorporation of the length of the reduced set, all which aid in electing better instances for the reduced set. To evaluate the performance of the proposed ÆIR algorithm, 18 benchmark datasets of various dimensions were used alongside 4 different classifiers: Random Forest, Decision Tree, K-Nearest Neighbor and Naïve Bayes. The experiments demonstrated the effectiveness of ÆIR in reducing the size of the instances. On an average, approximately 50 % reduction in the number instances was observed. This can result in an equivalent decline in terms of the storage requirements, while contemporaneously improving the prediction accuracy.