论文信息 - Ensemble Extreme Learning Machine for Multi-instance Learning

Ensemble Extreme Learning Machine for Multi-instance Learning

Multi-instance learning (MIL) is a classification approach for classifying on a collection of instances which each group is represented as a bag. The main task of MIL is to learn from labels and features of instances to produce a model to predict a label of a testing bag. Traditional MIL algorithms were proposed to address the MIL problem, but most of the algorithms take a large time scale for their training process since they have to computing the parameter tuning. To address the learning time problem, the multi-instance learning method based on extreme learning machine (ELM-MIL) was proposed. However, the randomly generated parameters of ELM-MIL may reduce its generalization performance. Therefore, we proposed a new method to improve the generalization performance of the ELM-MIL which the new method is based on the ensemble with majority voting approach named the ensemble extreme learning machine for multi-instance learning (E-ELM-MIL). To evaluate the new method, several benchmark datasets were studied in this paper. From experimental results show that E-ELM-MIL outperforms ELM-MIL and the other state of the art MIL algorithm.

Punyaphol Horata | Songpon Sastrawaha

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