Inductive Logic Programming for Structure-Activity Relationship Studies on Large Scale Data

Inductive Logic Programming (ILP) is a combination of inductive learning and first-order logic aiming to learn first-order hypotheses from training examples. ILP has a serious bottleneck in an intractably enormous hypothesis search space. This makes existing approaches perform poorly on large-scale real-world datasets. In this research, we propose a technique to make the system handle an enormous search space efficiently by deriving qualitative information into search heuristics. Currently, heuristic functions used in ILP systems are based only on quantitative information, e.g. number of examples covered and length of candidates. We focus on a kind of data consisting of several parts. The approach aims to find hypotheses describing each class by using both individual and relational features of parts. The data can be found in denoting chemical compound structure for StructureActivity Relationship studies (SAR). We apply the proposed method to extract rules describing chemical activi...

• Type: Conference
• Year: 2005
• Where: SAINT
• Authors: Cholwich Nattee, Sukree Sinthupinyo, Masayuki Numao, Takashi Okada
• Keywords: Inductive Logic Programming, Internet Technology, Real-world Dataset, SAINT 2005, Search Space