Particle Swarm based Data Mining Algorithms for classification tasks

Sousa, Tiago Ferra de; Silva, Arlindo; Neves, Ana

doi:10.1016/j.parco.2003.12.015

Cited by 268 publications

(135 citation statements)

References 7 publications

(8 reference statements)

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“…Review of Literature Silva and Neves (2004) was a seminal paper that suggested using Particle Swarm Optimization for performing data mining tasks. Variants based on Particle Swarm technique were developed and used for classification.…”

Section: Conventional Rule Mining Methodsmentioning

confidence: 99%

An Algorithm for Mining Usable Rules Using a Holistic Swarm Based Approach

Mangat

Vig

2014

Journal of Computer Science

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Evolutionary algorithms are capable of finding near optimal solutions to problems which are intractable to solve using conventional methods. One such problem is to accurately classify patients using rule mining methodology while controlling the size of output rules. A massive amount of data pertaining to medicine is generated and recorded daily. Uncovering useful knowledge and assisting decision makers in the diagnosis and treatment of diseases from this vast data has become imperative. Association rule mining is an obvious choice for representing this previously hidden information as rules are simple to understand and infer. These rules can be used to understand the etiology of diseases and classify patients based on recorded characteristics. The interestingness of such an algorithm for rule mining will be determined by its accuracy and ability to produce easily understandable rules. This study applies latest improvements in swarm intelligence to devise a novel strategy for rule mining that exhibits high predictive accuracy and comprehensibility. It has been applied over four medical datasets to classify patients as fit or unfit. The paper begins with an explanation of rule mining functionality and concept of swarm intelligence. The current techniques for rule mining in the medical domain are surveyed and their shortcomings are identified. This is followed by a description of the proposed algorithm which includes a novel rule discovery procedure and a novel rule list selection criterion. The results of the proposed algorithm thus obtained, are compared with the other best known approaches. Finally, the future scope of work in this area is briefly discussed.

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Section: Conventional Rule Mining Methodsmentioning

confidence: 99%

An Algorithm for Mining Usable Rules Using a Holistic Swarm Based Approach

Mangat

Vig

2014

Journal of Computer Science

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“…Sousa et al [61], [62] have proposed the use of PSO as a tool for data mining. In order to evaluate the usefulness of PSO for data mining, an empirical comparison of the performance of three variants of PSO with another evolutionary algorithm (Genetic Algorithm), in rule discovery for classification tasks is used.…”

Section: Swarm Intelligence and Knowledge Discoverymentioning

confidence: 99%

Swarm Intelligence in Data Mining

2006

Studies in Computational Intelligence

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“…PSO is also proposed in [28] as a new tool for Data Mining. According to [44], PSO proved to be a suitable candidate for classification tasks, as it can obtain competitive results against C4.5/J48. PSO is utilized in [24] to handle the problem of classification of instances in multiclass databases.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary framework using particle swarm optimization for classification method PROAFTN

Al-Obeidat¹,

Belacel²,

Carretero³

et al. 2011

Applied Soft Computing

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The aim of this paper is to introduce a methodology based on the particle swarm optimization (PSO) algorithm to train the Multi-Criteria Decision Aid (MCDA) method PROAFTN. PSO is an efficient evolutionary optimization algorithm using the social behavior of living organisms to explore the search space. It is a relatively new population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. Furthermore, it is easy to code and robust to control parameters. To apply PROAFTN, the values of several parameters need to be determined prior to classification, such as boundaries of intervals and weights. In this study, the proposed technique is named PSOPRO, which utilizes PSO to elicit the PROAFTN parameters from examples during the learning process. To test the effectiveness of the methodology and the quality of the obtained models, PSOPRO is evaluated on 12 public-domain datasets and compared with the previous work applied on PROAFTN. The computational results demonstrate that PSOPRO is very competitive with respect to the most common classification algorithms.

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Particle Swarm based Data Mining Algorithms for classification tasks

Cited by 268 publications

References 7 publications

An Algorithm for Mining Usable Rules Using a Holistic Swarm Based Approach

An Algorithm for Mining Usable Rules Using a Holistic Swarm Based Approach

Swarm Intelligence in Data Mining

An evolutionary framework using particle swarm optimization for classification method PROAFTN

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