距離によるヒューリスティックスを用いた帰納論理プログラミングと化学物質データマイニング(<特集>「アクティブマイニング」及び一般)

概要

論文の詳細を見る
We propose an approach for malting FOIL better handling multiple-instance data. This learning problem arises when trying to generate hypotheses from examples in the form of positive and negative bags. Each bag contains one or more instances and a bag is labelled as positive when there is at least one positive instance, otherwise, it is labelled as negative. Several algorithms have been proposed for learning in this framework. However all of them can only handle data in the attribute-value form which has limitations in knowledge representation. Therefore, it is difficult to handle examples consisting of structures among components, such as chemical compounds data. In this paper, the Diverse Density, a measure for multiple-instance data, is applied to adapt the heuristic function in FOIL in order to improve learning accuracy in multiple-instance data. We conducts the experiments on real-world data related to chemical compound analysis in order to show the improvement.
社団法人電子情報通信学会の論文
2003-09-09