mi-DS: Multiple-Instance Learning Algorithm

doi:10.1109/TSMCB.2012.2201468

mi-DS: Multiple-Instance Learning Algorithm

2013-02-01

18

PRE

AI

Rosalyn Hobson Hargraves

DOI：10.1109/TSMCB.2012.2201468

Original

Original request for help

Save

Abstract

En 中文

Multiple-instance learning (MIL) is a supervised learning technique that addresses the problem of classifying bags of instances instead of single instances. In this paper, we introduce a rule-based MIL algorithm, called mi-DS, and compare it with 21 existing MIL algorithms on 26 commonly used data sets. The results show that mi-DS performs on par with or better than several well-known algorithms and generates models characterized by balanced values of precision and recall. Importantly, the introduced method provides a framework that can be used for converting other rule-based algorithms into MIL algorithms.

Keywords:

Multiple-instance learning (MIL)

rule-based algorithms

supervised learning

mi-DS: Multiple-Instance Learning Algorithm

Abstract

Journal

Organization