Evolutionary Multi-Objective Multi-Tasking for Fuzzy Genetics-Based Machine Learning in Multi-Label Classification

Omozaki，Yuichi1; Masuyama，Naoki2; Nojima，Yusuke2; Ishibuchi，Hisao3

10.1109/FUZZ-IEEE55066.2022.9882681

2022

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

1098-7584

978-1-6654-6711-7

IEEE International Conference on Fuzzy Systems

2022-July

1-8

JUL 18-23, 2022

null,Padua,ITALY

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Explainable artificial intelligence (XAI) is an important research topic in the field of machine learning. A fuzzy rule-based classifier is a promising XAI technique thanks to its high interpretability. We can linguistically explain its classification result because a set of linguistically explainable fuzzy if-then rules are used for classification. In real-world data mining applications, multiple class labels are assigned to a single instance. Such a dataset is called a multi-label dataset (MLD). For MLDs, multiobjective fuzzy genetics-based machine learning for multi-label classification (MoFGBMLML) has been proposed. MoFGBMLML aims to search for explainable fuzzy classifiers by explicitly considering the accuracy-complexity tradeoff that exists in explainable classifier design. In the field of multi-label classification, different accuracy metrics have been proposed to evaluate classifier performance. As a result, different multiobjective optimization problems (MOPs) can be defined using each accuracy metric together with a complexity metric. Usually, MoFGBMLML solves each MOP independently. In this paper, we incorporate the idea of multi-tasking optimization into MoFGBMLML so that multiple MOPs are solved simultaneously. We also propose a new information sharing method to improve the effectiveness of multi-tasking optimization in MoFGBMLML. Our experimental results show that multiple accuracy metrics can be simultaneously optimized through the multi-tasking optimization framework and the proposed information sharing method improves the classification accuracy of fuzzy classifiers obtained by MoFGBMLML.

evolutionary multiobjective multi-tasking optimization fuzzy rule-based classification system multi-label classification multiobjective fuzzy genetics-based machine learning solution information sharing

Others

English

CPCI-S

Japan Society for the Promotion of Science (JSPS) KAKENHI[JP19K12159] ; National Natural Science Foundation of China[61876075] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Program for Guangdong Introducing Innovative and Enterpreneurial Teams[2017ZT07X386] ; Stable Support Plan Program of Shenzhen Natural Science Fund[20200925174447003] ; Shenzhen Science and Technology Program[KQTD2016112514355531]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic

WOS:000861288500067

2-s2.0-85138756774

Scopus

1.Osaka Prefecture University,Graduate School of Engineering,Osaka,Japan
2.Osaka Metropolitan University,Graduate School of Informatics,Osaka,Japan
3.Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,China

Omozaki，Yuichi,Masuyama，Naoki,Nojima，Yusuke,et al. Evolutionary Multi-Objective Multi-Tasking for Fuzzy Genetics-Based Machine Learning in Multi-Label Classification[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:1-8.