A sample set condensation algorithm for the class sensitive artificial neural network

Chen, C. H.; Jóźwik, A.

doi:10.1016/0167-8655(96)00041-4

Cited by 49 publications

(31 citation statements)

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“…where x 1 , x 2 , y 1 , y 2 are normal random variables whose means and variances are (0, 10), (10,5), (3,10) and (20,5), respectively. The total number of vectors per class is 500.…”

Section: Experimental Datamentioning

confidence: 99%

A brief taxonomy and ranking of creative prototype reduction schemes

Kim

Oommen

2003

Pattern Analysis & Applications

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Various Prototype Reduction Schemes (PRS) have been reported in the literature. Based on their operating characteristics, these schemes fall into two fairly distinct categories -those which are of a creative sort, and those which are essentially selective. The norms for evaluating these methods are typically, the reduction rate and the classification accuracy. It is generally believed that the former class of methods is superior to the latter.In this paper, we report the results of executing various creative PRS, and attempt to comparatively quantify their capabilities. The paper presents a brief taxonomy of the various reported PRS schemes. Our experimental results for three artificial data sets, and for samples involving real-life data sets, demonstrate that no single method is uniformly superior to the others for all kinds of applications. This result, though consistent with the findings of [2], is, in one sense, counter-intuitive, because the various researchers have presented their specific PRS with the hope that it would be superior to the previously reported methods. But the fact is that while one method is superior in certain domains, it is inferior to another method when dealing with a data set with markedly different characteristics. The conclusion of this study is that the question of determining when one method is superior to another remains open. Indeed, it appears as if the designers of the Pattern Recognition system will have to choose the appropriate PRS based to the specific characteristics of the data that they are studying. The paper also suggests answers to various hypotheses that relate to the accuracies and reduction rates of families of PRS.

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“…where x 1 , x 2 , y 1 , y 2 are normal random variables whose means and variances are (0, 10), (10,5), (3,10) and (20,5), respectively. The total number of vectors per class is 500.…”

Section: Experimental Datamentioning

confidence: 99%

A brief taxonomy and ranking of creative prototype reduction schemes

Kim

Oommen

2003

Pattern Analysis & Applications

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“…Some other algorithms artificially generate prototypes in locations accurately determined in order to reduce the TS size. Within this category, we can find the algorithms presented by Chang [3] and by Chen and Józwik [6].…”

Section: Prototype Selectionmentioning

confidence: 99%

“…Chen and Józwik [6] proposed an algorithm which consists of dividing the TS into some subsets using the concept of diameter of a set (distance between the two farthest points). It starts by partitioning the TS into two subsets by the middle point between the two farthest cases.…”

Section: Prototype Selectionmentioning

confidence: 99%

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Using the Geometrical Distribution of Prototypes for Training Set Condensing

Lozano

Sánchez

Pla

2004

Current Topics in Artificial Intelligence

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Abstract. In this paper, some new approaches to training set size reduction are presented. These schemes basically consist of defining a small number of prototypes that represent all the original instances. Although the ultimate aim of the algorithms proposed here is to obtain a strongly reduced training set, the performance is empirically evaluated over nine real datasets by comparing the reduction rate and the classification accuracy with those of other condensing techniques.

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“…Well known methods for PG are PNN [28], learning quantization vector (LVQ) [29], Chen's algorithm [30], ICPL [27], HYB [31] and MixtGauss [32]. A good study of PS and PG can be found in [33].…”

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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A sample set condensation algorithm for the class sensitive artificial neural network

Cited by 49 publications

References 1 publication

A brief taxonomy and ranking of creative prototype reduction schemes

A brief taxonomy and ranking of creative prototype reduction schemes

Using the Geometrical Distribution of Prototypes for Training Set Condensing

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

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