Ant Colony Prototype Reduction Algorithm for kNN Classification
Amal Miloud-Aouidate, Ahmed Riadh Baba-Ali · 2012
Ant colony optimization (ACO) techniques were proposed to solve many combinatorial problems. As prototype selection (PS) is a combinatorial problem we attempt, in this paper, to address it using an Ant Colony algorithm. This work proposes an Ant Prototype Reducing algorithm (Ant-PR). The goal of this algorithm is to reduce the training set of the 1NN classifier. We compared the Ant-PR performances to two classical well-known kNN condensing algorithms. The results provide evidence that: (1) Ant-PR is competitive with the well-known kNN algorithms, (2) The condensed sets computed by Ant-PR offers better classification accuracy then those obtained by the compared algorithms.