Authors: Juan Li; Yuping Wang
Addresses: School of Computer Science and Technology, Xidian University Xi'an, Shaanxi 710071, China; School of Distance Education, Shaanxi Normal University, Xi'an, Shaanxi 710062, China ' School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
Abstract: Prototype selection aims at reducing the storage of datasets and execution time, and improving prediction accuracy and operation efficiency by removing noisy or redundant samples, so the prediction classification accuracy is maximised and the reduction ratio is minimised simultaneously. To achieve this purpose, a two objective optimisation model is set up for prototype selection problem in the paper. To make the model be solved easier, it is transformed to a single objective optimisation model by the division of the two objectives, and a new two-layer genetic algorithm is proposed by using a divide-and-conquer partition strategy. The divide-and-conquer partition can divide the whole dataset into some random sub-datasets to be handled, respectively. The simulations are conducted and the proposed algorithm is compared with several existing algorithms. The results obtained on UCI static datasets and time series datasets indicate that the proposed algorithm is an expedient method in design nearest neighbour classifiers.
Keywords: machine learning; prototype selection; multi-objective optimisation; optional two-layer genetic algorithms; divide-and-conquer partition; simulation; classifier design; nearest neighbour classifiers.
International Journal of Sensor Networks, 2015 Vol.17 No.3, pp.163 - 176
Received: 11 Jun 2014
Accepted: 24 Jun 2014
Published online: 19 Mar 2015 *