Affiliations: [a] Key Lab. of Machine Learning and Computational Intelligence, College of Mathematics and Information Science, Hebei University, Baoding, China | [b] College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, China
Abstract: Motivated by the idea of cross-validation, a novel instance selection algorithm is proposed in this paper. The novelties of the proposed algorithm are that (1) it cross selects the important instances from the original data set with a committee, (2) it can deal with the problem of selecting instance from large data sets. We experimentally compared our algorithm with five state-of-the-art approaches which are CNN, ENN, RNN, MCS, and ICF on 3 artificial data sets and 6 UCI data sets, including 4 large data sets, ranking from 130K to 4898K in size. The experimental results show that the proposed algorithm is very efficient and effective, especially on large data sets.
Keywords: Instances selection, extreme learning machine, K–L divergence, large data sets
