Scale factor local search in differential evolution

Neri, Ferrante; Tirronen, Ville

doi:10.1007/s12293-009-0008-9

Cited by 162 publications

(61 citation statements)

References 33 publications

(50 reference statements)

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“…Scale Factor Local Search in Differential Evolution (SFLSDE) [28] was shown to be one of the top performing PG methods in the experimental study of [23]. It is a positioning adjustment algorithm, optimizing the positions of the instances in the dataset.…”

Section: Scale Factor Local Search In Differential Evolutionmentioning

confidence: 99%

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EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data

et al. 2016

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Classification problems with an imbalanced class distribution have received an increased amount of attention within the machine learning community over the last decade. They are encountered in a growing number of realworld situations and pose a challenge to standard machine learning techniques. We propose a new hybrid method specifically tailored to handle class imbalance, called EPRENNID. It performs an evolutionary prototype reduction focused on providing diverse solutions to prevent the method from overfitting the training set. It also allows us to explicitly reduce the underrepresented class, which the most common preprocessing solutions handling class imbalance usually protect. As part of the experimental study, we show that the proposed prototype reduction method outperforms state-of-the-art preprocessing techniques. The preprocessing step yields multiple prototype sets that are later used in an ensemble, performing a weighted voting scheme with the nearest neighbor classifier. EPRENNID is experimentally shown to significantly outperform previous proposals.

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Section: Scale Factor Local Search In Differential Evolutionmentioning

confidence: 99%

“…When updating the scale factors F i , two local searches are used: the golden section search and hill-climbing. We refer to [28] for further details.…”

Section: Scale Factor Local Search In Differential Evolutionmentioning

confidence: 99%

EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data

et al. 2016

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“…Scale factor local search in differential evolution was proposed by Neri and Tirronen [53]. This self-adaptive algorithm was inspired by memetic algorithms.…”

Section: Scale Factor Local Search In Differential Evolution (Sflsde)mentioning

confidence: 99%

“…In [53], the authors only use the DE/rand/1/Bin mutation strategy. In the experimental study, we incorporate other mutation strategies.…”

Section: Scale Factor Local Search In Differential Evolution (Sflsde)mentioning

confidence: 99%

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

Triguero

García

Herrera

2011

Pattern Recognition

109

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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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Differential Evolution

Qing¹

2009

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A wide number of real word applications presents a class distribution where examples belonging to one class heavily outnumber the examples in the other class. This is an arduous situation where standard classification techniques usually decrease their performance, creating a handicap to correctly identify the minority class, which is precisely the case under consideration in these applications.In this work, we propose the usage of the Iterative Instance Adjustment for Imbalanced Domains (IPADE-ID) algorithm. It is an evolutionary framework, which uses an instance generation technique, designed to face the existing imbalance modifying the original training set. The method, iteratively learns the appropriate number of examples that represent the classes and their particular positioning. The learning process contains three key operations in its design: a customized initialization procedure, an evolutionary optimization of the positioning of the examples and a selection of the most representative examples for each class.An experimental analysis is carried out with a wide range of highly imbalanced datasets over the proposal and recognized solutions to the problem. The results obtained, which have been contrasted through nonparametric statistical tests, show that our proposal outperforms previously proposed methods.

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Scale factor local search in differential evolution

Cited by 162 publications

References 33 publications

EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data

EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data

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

Differential Evolution

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