Cross-validation in Fuzzy ARTMAP for large databases

Koufakou, Anna; Georgiopoulos, Michael; Anagnostopoulos, G; Kasparis, Takis

doi:10.1016/s0893-6080(01)00090-9

Cited by 30 publications

(26 citation statements)

References 24 publications

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“…Training with validation is a popular approach in the machine learning community aiming at the elimination of overfitting in order to enhance generalization [5]. This approach also regards the usage of training until completion as erroneous as it is prone to overfitting [23]. On the other hand, the developers of fuzzy ARTMAP defined training until completion as the method of choice and it is used for fuzzy ARTMAP classifiers frequently [7], [23].…”

Section: A the Methodologymentioning

confidence: 99%

Accurate and Fast Off and Online Fuzzy ARTMAP-Based Image Classification With Application to Genetic Abnormality Diagnosis

Vigdor

Lerner

2006

IEEE Trans. Neural Netw.

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Abstract-We propose and investigate the fuzzy ARTMAP neural network in off and online classification of fluorescence in situ hybridization image signals enabling clinical diagnosis of numerical genetic abnormalities. We evaluate the classification task (detecting a several abnormalities separately or simultaneously), classifier paradigm (monolithic or hierarchical), ordering strategy for the training patterns (averaging or voting), training mode (for one epoch, with validation or until completion) and model sensitivity to parameters. We find the fuzzy ARTMAP accurate in accomplishing both tasks requiring only very few training epochs. Also, selecting a training ordering by voting is more precise than if averaging over orderings. If trained for only one epoch, the fuzzy ARTMAP provides fast, yet stable and accurate learning as well as insensitivity to model complexity. Early stop of training using a validation set reduces the fuzzy ARTMAP complexity as for other machine learning models but cannot improve accuracy beyond that achieved when training is completed. Compared to other machine learning models, the fuzzy ARTMAP does not loose but gain accuracy when overtrained, although increasing its number of categories. Learned incrementally, the fuzzy ARTMAP reaches its ultimate accuracy very fast obtaining most of its data representation capability and accuracy by using only a few examples. Finally, the fuzzy ARTMAP accuracy for this domain is comparable with those of the multilayer perceptron and support vector machine and superior to those of the naive Bayesian and linear classifiers.Index Terms-Fluorescence in situ hybridization (FISH), fuzzy ARTMAP neural network (NN), genetic abnormality diagnosis, image classification, off-and online learning.

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Section: A the Methodologymentioning

confidence: 99%

Accurate and Fast Off and Online Fuzzy ARTMAP-Based Image Classification With Application to Genetic Abnormality Diagnosis

Vigdor

Lerner

2006

IEEE Trans. Neural Netw.

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“…Amongst them we refer to the work by Marriott and Harrisson (1995), where the authors eliminate the match tracking mechanism of Fuzzy ARTMAP when dealing with noisy data; the work by Charalampidis, et al (2001), where the Fuzzy ARTMAP equations are appropriately modified to compensate for noisy data; the work by Verzi, et al (2001), Anagnostopoulos, et al (2002bAnagnostopoulos, et al ( , 2003, and Gomez-Sanchez, et al (2002 & 2001), where different ways are introduced of allowing the Fuzzy ARTMAP categories to encode patterns that are not necessarily mapped to the same label; the work by Koufakou, et al (2001), where cross-validation is employed to avoid the overtraining/category proliferation problem in Fuzzy ARTMAP; and the work by Carpenter (1998), Williamson (1997), Parrado-Hernandez et al (2003), where the ART structure is changed from a winner-take-all to a distributed version and simultaneously slow learning is employed with the intent of creating fewer ART categories and reducing the effects of noisy patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

GFAM: Evolving Fuzzy ARTMAP neural networks

et al. 2007

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This paper focuses on the evolution of Fuzzy ARTMAP neural network classifiers, using genetic algorithms, with the objective of improving generalization performance (classification accuracy of the ART network on unseen test data) and alleviating the ART category proliferation problem (the problem of creating more than necessary ART network categories to solve a classification problem). We refer to the resulting architecture as GFAM. We demonstrate through extensive experimentation that GFAM exhibits good generalization and is of small size (creates few ART categories), while consuming reasonable computational effort. In a number of classification problems, GFAM produces the optimal classifier. Furthermore, we compare the performance of GFAM with other competitive ARTMAP classifiers that have appeared in the literature and addressed the category proliferation problem in ART. We illustrate that GFAM produces improved results over these architectures, as well as other competitive classifiers.

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“…It has been associated with the presence of noise [17] and with the inefficiency of the category geometry [3], which requires an excessive number of categories in order to cover the input space. Category proliferation has been also associated [18] with overtraining, and cross validation [19] has been suggested as a solution. DAM [4] proposes distributed learning as another solution to the category proliferation problem.…”

Section: Introductionmentioning

confidence: 99%

Polytope ARTMAP: Pattern Classification Without Vigilance Based on General Geometry Categories

Amorim

Delgado

Ameneiro

2007

IEEE Trans. Neural Netw.

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Abstract-This paper proposes polytope ARTMAP (PTAM), an adaptive resonance theory (ART) network for classification tasks which does not use the vigilance parameter. This feature is due to the geometry of categories in PTAM, which are irregular polytopes whose borders approximate the borders among the output predictions. During training, the categories expand only towards the input pattern without category overlap. The category expansion in PTAM is naturally limited by the other categories, and not by the category size, so the vigilance is not necessary. PTAM works in a fully automatic way for pattern classification tasks, without any parameter tuning, so it is easier to employ for nonexpert users than other classifiers. PTAM achieves lower error than the leading ART networks on a complete collection of benchmark data sets, except for noisy data, without any parameter optimization.

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Cross-validation in Fuzzy ARTMAP for large databases

Cited by 30 publications

References 24 publications

Accurate and Fast Off and Online Fuzzy ARTMAP-Based Image Classification With Application to Genetic Abnormality Diagnosis

Accurate and Fast Off and Online Fuzzy ARTMAP-Based Image Classification With Application to Genetic Abnormality Diagnosis

GFAM: Evolving Fuzzy ARTMAP neural networks

Polytope ARTMAP: Pattern Classification Without Vigilance Based on General Geometry Categories

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