An Inductive Logic Programming Algorithm Based on Artificial Bee Colony

作     者：Li, Yanjuan Niu, Mengting Guo, Jifeng 

作者机构：Northeast Forestry Univ Coll Informat & Comp Engn Harbin Heilongjiang Peoples R China 

出 版 物：《JOURNAL OF INFORMATION TECHNOLOGY RESEARCH》 (国际信息技术研究杂志)

年 卷 期：2019年第12卷第1期

页      面：89-104页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Fundamental Research Funds for the Central Universities [2572017CB33] China Postdoctoral Science Foundation Heilongjiang Postdoctoral Fund [LBH-Z13014] 

主　　题：Artificial bee colony Deterministic search Inductive logic programming Machine learning Stochastic search 

摘      要：Inductive logic programming (ILP) is a hot research field in machine learning. Although ILP has obtained great success in many domains, in most ILP system, deterministic search are used to search the hypotheses space, and they are easy to trap in local optima. To overcome the shortcomings, an ILP system based on artificial bee colony (ABCILP) is proposed in this article. ABCILP adopts an ABC stochastic search to examine the hypotheses space, the shortcoming of deterministic search is conquered by stochastic search. ABCILP regard each first-order rule as a food source and propose some discrete operations to generate the neighborhood food sources. A new fitness is proposed and an adaptive strategy is adopted to determine the parameter of the new fitness. Experimental results show that: 1) the proposed new fitness function can more precisely measure the quality of hypothesis and can avoid generating an over-specific rule;2) the performance of ABCILP is better than other systems compared with it.