Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves

作     者：Goadrich, Mark Oliphant, Louis Shavlik, Jude 

作者机构：Univ Wisconsin Dept Biostat & Med Informat Dept Comp Sci Madison WI 53706 USA 

出 版 物：《MACHINE LEARNING》 (机器学习)

年 卷 期：2006年第64卷第1-3期

页      面：231-261页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：inductive logic programming ensembles recall-precision curves biomedical information extraction 

摘      要：Many domains in the field of Inductive Logic Programming (ILP) involve highly unbalanced data. A common way to measure performance in these domains is to use precision and recall instead of simply using accuracy. The goal of our research is to find new approaches within ILP particularly suited for large, highly-skewed domains. We propose Gleaner, a randomized search method that collects good clauses from a broad spectrum of points along the recall dimension in recall-precision curves and employs an at least L of these K clauses thresholding method to combine sets of selected clauses. Our research focuses on Multi-Slot Information Extraction (IE), a task that typically involves many more negative examples than positive examples. We formulate this problem into a relational domain, using two large testbeds involving the extraction of important relations from the abstracts of biomedical journal articles. We compare Gleaner to ensembles of standard theories learned by Aleph, finding that Gleaner produces comparable testset results in a fraction of the training time.