AMPSO: A New Particle Swarm Method for Nearest Neighborhood Classification

Cervantes, Alejandro; Galván, Inés María; Isasi, Pedro

doi:10.1109/tsmcb.2008.2011816

Cited by 99 publications

(45 citation statements)

References 24 publications

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“…The numbers of research papers and results of experiments show the PSO is competitive to the standard classification algorithms[9]- [14].…”

Section: Pso For Classificationmentioning

confidence: 99%

Semi-supervised Classification: Cluster and Label Approach using Particle Swarm Optimization

Azab¹,

Hady²,

Hefny³

2017

IJCA

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Classification predicts classes of objects using the knowledge learned during the training phase. This process requires learning from labeled samples. However, the labeled samples usually limited. Annotation process is annoying, tedious, expensive, and requires human experts. Meanwhile, unlabeled data is available and almost free. Semi-supervised learning approaches make use of both labeled and unlabeled data. This paper introduces cluster and label approach using PSO for semi-supervised classification. PSO is competitive to traditional clustering algorithms. A new local best PSO is presented to cluster the unlabeled data. The available labeled data guides the learning process. The experiments are conducted using four state-of-the-art datasets from different domains. The results compared with Label Propagation a popular semi-supervised classifier and two state-of-the-art supervised classification models, namely k-nearest neighbors and decision trees. The experiments show the efficiency of the proposed model.

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“…The numbers of research papers and results of experiments show the PSO is competitive to the standard classification algorithms[9]- [14].…”

Section: Pso For Classificationmentioning

confidence: 99%

Semi-supervised Classification: Cluster and Label Approach using Particle Swarm Optimization

Azab¹,

Hady²,

Hefny³

2017

IJCA

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“…Among the studies that have approached the PG problem, those based on bioinspired optimization have gained popularity in recent years [1][2][3][4]6,[8][9][10]. These generally seek at optimizing a criterion related to the classification performance of the generated prototypes.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Multi-Objective Approach for Prototype Generation and Feature Selection

Rosales-Pérez

González

Coello

et al. 2014

Advanced Information Systems Engineering

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Abstract. This paper introduces EMOPG+FS, a novel approach to prototype generation and feature selection that explicitly minimizes the classification error rate, the number of prototypes, and the number of features. Under EMOPG+FS, prototypes are initialized from a subset of training instances, whose positions are adjusted through a multiobjective evolutionary algorithm. The optimization process aims to find a set of suitable solutions that represent the best possible trade-offs among the considered criteria. Besides this, we also propose a strategy for selecting a single solution from the several that are generated during the multi-objective optimization process.We assess the performance of our proposed EMOPG+FS using a suite of benchmark data sets and we compare its results with respect to those obtained by other evolutionary and non-evolutionary techniques. Our experimental results indicate that our proposed approach is able to achieve highly competitive results.

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“…Evolutionary algorithms (EAs) [34] have been successfully used in different data mining problems [35,36]. Given that PS and PG problems could be seen as combinatorial and optimization problems, EAs have been used to solve them with excellent results [37][38][39][40]. PS can be expressed as a binary space search problem and, as far as we know, the best evolutionary model proposed for PS is based on memetic algorithms [41] and is called SSMA [38].…”

Section: Introductionmentioning

confidence: 99%

“…In fact, PSO has been satisfactorily used for prototype adjustment [39,40]. The first attempts at using DE for PG can be found in [48].…”

Section: Introductionmentioning

confidence: 99%

Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification

Triguero

García

Herrera

2011

Pattern Recognition

112

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a b s t r a c tNearest neighbor classification is one of the most used and well known methods in data mining. Its simplest version has several drawbacks, such as low efficiency, high storage requirements and sensitivity to noise. Data reduction techniques have been used to alleviate these shortcomings. Among them, prototype selection and generation techniques have been shown to be very effective. Positioning adjustment of prototypes is a successful trend within the prototype generation methodology.Evolutionary algorithms are adaptive methods based on natural evolution that may be used for searching and optimization. Positioning adjustment of prototypes can be viewed as an optimization problem, thus it can be solved using evolutionary algorithms. This paper proposes a differential evolution based approach for optimizing the positioning of prototypes. Specifically, we provide a complete study of the performance of four recent advances in differential evolution. Furthermore, we show the good synergy obtained by the combination of a prototype selection stage with an optimization of the positioning of prototypes previous to nearest neighbor classification. The results are contrasted with non-parametrical statistical tests and show that our proposals outperform previously proposed methods.

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AMPSO: A New Particle Swarm Method for Nearest Neighborhood Classification

Cited by 99 publications

References 24 publications

Semi-supervised Classification: Cluster and Label Approach using Particle Swarm Optimization

Semi-supervised Classification: Cluster and Label Approach using Particle Swarm Optimization

Evolutionary Multi-Objective Approach for Prototype Generation and Feature Selection

Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification

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