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A study of different quality evaluation functions in the cAnt-Miner(PB) classification algorithm

Authors:

Matthew Medland,

Fernando OteroAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

Pages 49 - 56

https://doi.org/10.1145/2330163.2330171

Published: 07 July 2012 Publication History

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Abstract

Ant colony optimization (ACO) algorithms for classification in general employ a sequential covering strategy to create a list of classification rules. A key component in this strategy is the selection of the rule quality function, since the algorithm aims at creating one rule at a time using an ACO-based procedure to search the best rule. Recently, an improved strategy has been proposed in the cAnt-Miner_PB algorithm, where an ACO-based procedure is used to create a complete list of rules instead of individual rules. In the cAnt-Miner_PB algorithm, the rule quality function has a smaller role and the search is guided by the quality of a list of rules. This paper sets out to determine the effect of different rule and list quality functions in terms of both predictive accuracy and size of the discovered model in cAnt-Miner_PB. The comparative analysis is performed using 12 data sets from the UCI Machine Learning repository and shows that the effect of the rule quality functions in cAnt-Miner_PB is different from the results previously presented in the literature.

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Cited By

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Chennupati GAzad RRyan CArnold D(2014)Predict the performance of GE with an ACO based machine learning algorithmProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2609860(1353-1360)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2609860
Chennupati GRyan CAzad RArnold D(2014)Predict the success or failure of an evolutionary algorithm runProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2598471(131-132)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2598471
Salama KFreitas AAlba E(2013)Evaluating the use of different measure functions in the predictive quality of ABC-minerProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2464580(15-16)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2464580
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Index Terms

A study of different quality evaluation functions in the cAnt-Miner(PB) classification algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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Published In

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

July 2012

1396 pages

ISBN:9781450311779

DOI:10.1145/2330163

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2012

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GECCO '12

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SIGEVO

GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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Cited By

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Chennupati GAzad RRyan CArnold D(2014)Predict the performance of GE with an ACO based machine learning algorithmProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2609860(1353-1360)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2609860
Chennupati GRyan CAzad RArnold D(2014)Predict the success or failure of an evolutionary algorithm runProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2598471(131-132)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2598471
Salama KFreitas AAlba E(2013)Evaluating the use of different measure functions in the predictive quality of ABC-minerProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2464580(15-16)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2464580
Salama KFreitas A(2013)Investigating the impact of various classification quality measures in the predictive accuracy of ABC-Miner2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557846(2321-2328)Online publication date: Jun-2013
https://doi.org/10.1109/CEC.2013.6557846

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