title: A novel multiple instance learning method based on extreme learning machine.
abstract: Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm. As a learning paradigm, MIL is different from traditional supervised learning that handles the classification of bags comprising unlabeled instances. In this paper, a novel efficient method based on extreme learning machine (ELM) is proposed to address MIL problem. First, the most qualified instance is selected in each bag through a single hidden layer feedforward network (SLFN) whose input and output weights are both initialed randomly, and the single selected instance is used to represent every bag. Second, the modified ELM model is trained by using the selected instances to update the output weights. Experiments on several benchmark data sets and multiple instance regression data sets show that the ELM-MIL achieves good performance; moreover, it runs several times or even hundreds of times faster than other similar MIL algorithms.
authors.affiliation: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China.
authors.forename: Liangjian
authors.initials: L
authors.lastname: Cai
authors.affiliation: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China.
authors.forename: Jinzhu
authors.identifier: 0000-0002-2823-6571
authors.initials: J
authors.lastname: Peng
authors.affiliation: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China.
authors.forename: Yuheng
authors.initials: Y
authors.lastname: Jia
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods