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Solving test case based problems with fuzzy dominance

Published: 01 July 2017 Publication History

Abstract

Genetic algorithms and genetic programming lend themselves well to the field of machine learning, which involves solving test case based problems. However, most traditional multi-objective selection methods work with scalar objectives, such as minimizing false negative and false positive rates, that are computed from underlying test cases.
In this paper, we propose a new fuzzy selection operator that takes into account the statistical nature of machine learning problems based on test cases. Rather than use a Pareto rank or strength computed from scalar objectives, such as with NSGA2 or SPEA2, we will compute a probability of Pareto optimality. This will be accomplished through covariance estimation and Markov chain Monte Carlo simulation in order to generate probabilistic objective scores for each individual. We then compute a probability that each individual will generate a Pareto optimal solution. This probability is directly used with a roulette wheel selection technique.
Our method's performance is evaluated on the evolution of a feature selection vector for a binary classification on each of eight different activities. Fuzzy selection performance varies, outperforming both NSGA2 and SPEA2 in both speed (measured in generations) and solution quality (measured by area under the curve) in some cases, while underperforming in others.

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Cited By

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  • (2021)An Automatic Test Sequence Generation Method Based on Markov Chain Model2021 World Conference on Computing and Communication Technologies (WCCCT)10.1109/WCCCT52091.2021.00024(91-96)Online publication date: Jan-2021

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cover image ACM Conferences
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference
July 2017
1427 pages
ISBN:9781450349208
DOI:10.1145/3071178
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 July 2017

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Author Tags

  1. genetic algorithms
  2. machine learning
  3. markov chain monte carlo
  4. pareto dominance

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GECCO '17 Paper Acceptance Rate 178 of 462 submissions, 39%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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  • (2021)An Automatic Test Sequence Generation Method Based on Markov Chain Model2021 World Conference on Computing and Communication Technologies (WCCCT)10.1109/WCCCT52091.2021.00024(91-96)Online publication date: Jan-2021

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