Rule Induction on Multiple Instance Learning Concepts

Master thesis (2022)

Authors

R. van der Wal Electrical Engineering, Mathematics and Computer Science

Contributors

D.M.J. Tax Pattern Recognition and Bioinformatics - EEMCS (mentor)
M.J.T. Reinders Pattern Recognition and Bioinformatics - EEMCS (graduation committee member)
H. Wang (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Robin van der Wal
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Published Date

25-10-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Multiple Instance Learning (MIL) is a type of semi-supervised machine learning used recently in medical and multi-media fields. In MIL, instead of a single feature vector, a set of feature vectors has to be classified. Standard MIL algorithms assume that only some of these vectors are useful for building a classifier. This paper extends the standard MIL assumption by combining propositional logic and classical MIL classifiers. Adding propositional logic allows for increased interpretability as it establishes an if-then relationship between the input data and the output classes. This combination of logic and classical MIL classifiers will be called Concept Rule Induction (CRI). CRI is tested on several artificial and real-life bird song data. CRI is shown to work for these data sets, and the rules produced by propositional logic can be interpreted