Applying non-dominated sorting genetic algorithm ii to multi-objective optimization of a weighted multi-metric distance for performing data mining tasks

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

3 Citations (Scopus)

Abstract

Multi-objective optimization (MOO) is a class of optimization problems where several objective functions must be simultaneously optimized. Traditional search methods are difficult to extend to MOO problems so many of these problems are solved using bio-inspired optimization algorithms. One of the famous optimization algorithms that have been applied to MOO is the nondominated sorting genetic algorithm II (NSGA-II). NSGA-II algorithm has been successfully used to solve MOO problems owing to its lower computational complexity compared with the other optimization algorithms. In this paper we use NSGA-II to solve a MOO problem of time series data mining. The problem in question is determining the optimal weights of a multi-metric distance that is used to perform several data mining tasks. NSGA-II is particularly appropriate to optimize data mining problems where fitness functions evaluation usually involves intensive computing resources. Whereas several previous papers have proposed different methods to optimize time series data mining problems, this paper is, to our knowledge, the first paper to optimize several time series data mining tasks simultaneously. The experiments we conducted show that the performance of the optimized combination of multi-metric distances we propose in executing time series data mining tasks is superior to that of the distance metrics that constitute the combination when they are applied separately.

Original languageEnglish
Title of host publicationApplications of Evolutionary Computation - 18th European Conference, EvoApplications 2015, Proceedings
EditorsGiovanni Squillero, Antonio M. Mora
PublisherSpringer-Verlag Italia
Pages579-589
Number of pages11
ISBN (Electronic)9783319165493
ISBN (Print)9783319165486
DOIs
Publication statusPublished - 2015
Event18th European Conference on the Applications of Evolutionary Computation, EvoApplications 2015 - Copenhagen, Denmark
Duration: 8 Apr 201510 Apr 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9028
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th European Conference on the Applications of Evolutionary Computation, EvoApplications 2015
CountryDenmark
CityCopenhagen
Period8/04/1510/04/15

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Keywords

  • Classification
  • Clustering
  • Data mining
  • Multi-metric
  • Multi-objective optimization
  • Time series

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Fuad, M. M. M. (2015). Applying non-dominated sorting genetic algorithm ii to multi-objective optimization of a weighted multi-metric distance for performing data mining tasks. In G. Squillero, & A. M. Mora (Eds.), Applications of Evolutionary Computation - 18th European Conference, EvoApplications 2015, Proceedings (pp. 579-589). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9028). Springer-Verlag Italia. https://doi.org/10.1007/978-3-319-16549-3_47