Generalization, discrimination, and multiple categorization using adaptive resonance theory

Lavoie, P.; Crespo, Jesús F.; Savaria, Yvon

doi:10.1109/72.774213

Cited by 14 publications

(3 citation statements)

References 18 publications

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“…The second subnetwork is placed at the next layer and usually divides the input space into 16-32 categories, which indicates a slightly more detailed classification of the input space. The last subnetwork in our self-organizing neural network will be placed at the lowest layer and classifies all the input patterns into either a motif or a nonmotif category with one or a few patterns [37]. Typically, the number of output neurons will be large for the last subnetwork and gradually reduced to a small number for the first subnetwork.…”

Section: B a New Structure Of Self-organizing Neural Networkmentioning

confidence: 99%

Motif Discoveries in Unaligned Molecular Sequences using Self-Organizing Neural Networks

Liu

Xiong

DasGupta

et al. 2006

IEEE Trans. Neural Netw.

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In this paper, we study the problem of motif discoveries in unaligned DNA and protein sequences. The problem of motif identification in DNA and protein sequences has been studied for many years in the literature. Major hurdles at this point include computational complexity and reliability of the search algorithms. We propose a self-organizing neural network structure for solving the problem of motif identification in DNA and protein sequences. Our network contains several layers, with each layer performing classifications at different levels. The top layer divides the input space into a small number of regions and the bottom layer classifies all input patterns into motifs and nonmotif patterns. Depending on the number of input patterns to be classified, several layers between the top layer and the bottom layer are needed to perform intermediate classifications. We maintain a low computational complexity through the use of the layered structure so that each pattern's classification is performed with respect to a small subspace of the whole input space. Our self-organizing neural network will grow as needed (e.g., when more motif patterns are classified). It will give the same amount of attention to each input pattern and will not omit any potential motif patterns. Finally, simulation results show that our algorithm outperforms existing algorithms in certain aspects. In particular, simulation results show that our algorithm can identify motifs with more mutations than existing algorithms. Our algorithm works well for long DNA sequences as well.

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Section: B a New Structure Of Self-organizing Neural Networkmentioning

confidence: 99%

Motif Discoveries in Unaligned Molecular Sequences using Self-Organizing Neural Networks

Liu

Xiong

DasGupta

et al. 2006

IEEE Trans. Neural Netw.

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“…Generally, the hierarchical relationships between ART modules are defined implicitly by the input signal flow, explicitly by enforcing constraints or connections, and/or by the setting of multiple vigilance parameters to define hierarchies. Alternatively, hierarchies within the same ART can be created by designing custom ART activation functions [40,41] or by analyzing its distributed activation patterns [42]. ART-based hierarchical approaches have been successfully applied, for instance, in text mining [20,43] and robotics [30,39].Another branch of clustering includes multi-prototype-based methods.…”

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confidence: 99%

Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence

2020

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This paper presents a novel adaptive resonance theory (ART)-based modular architecture for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA). DDVFA consists of a global ART system whose nodes are local fuzzy ART modules. It is equipped with the distinctive features of distributed higher-order activation and match functions, using dual vigilance parameters responsible for cluster similarity and data quantization. Together, these allow DDVFA to perform unsupervised modularization, create multi-prototype clustering representations, retrieve arbitrarily-shaped clusters, and control its compactness. Another important contribution is the reduction of order-dependence, an issue that affects any agglomerative clustering method. This paper demonstrates two approaches for mitigating order-dependence: preprocessing using visual assessment of cluster tendency (VAT) or postprocessing using a novel Merge ART module. The former is suitable for batch processing, whereas the latter can be used in online learning. Experimental results in the online learning mode carried out on 30 benchmark data sets show that DDVFA cascaded with Merge ART statistically outperformed the best other ART-based systems when samples were randomly presented. Conversely, they were found to be statistically equivalent in the offline mode when samples were pre-processed using VAT. Remarkably, performance comparisons to non-ART-based clustering algorithms show that DDVFA (which learns incrementally) was also statistically equivalent to the non-incremental (offline) methods of DBSCAN, single linkage hierarchical agglomerative clustering (HAC), and offline version of k-means, while retaining the appealing properties of ART. Links to the source code and data are provided. Considering the algorithm's simplicity, online learning capability, and performance, it is an ideal choice for many agglomerative clustering applications.Representation, Topology, Visual Assessment of Cluster Tendency. methods) and top-down (divisive or splitting methods) [2]. Hierarchical ART architectures generally follow two main designs [20]: (a) a series/cascade of ART modules where the output of one ART (i.e., a prototype) is the input of the next [21][22][23][24][25][26][27][28][29][30][31] or (b) parallel ART modules sharing the same inputs and using different vigilance values [6,[32][33][34][35][36][37][38][39]. Generally, the hierarchical relationships between ART modules are defined implicitly by the input signal flow, explicitly by enforcing constraints or connections, and/or by the setting of multiple vigilance parameters to define hierarchies. Alternatively, hierarchies within the same ART can be created by designing custom ART activation functions [40,41] or by analyzing its distributed activation patterns [42]. ART-based hierarchical approaches have been successfully applied, for instance, in text mining [20,43] and robotics [30,39].Another branch of clustering includes multi-prototype-based methods. These allow multiple prototypes to represent a si...

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“…Lavoie et al [43] proposed modifying the FAM choice function by adding an attentional tuning parameter. Using this parameter, the network can learn and use different categories with different degrees of generality, hence enabling mutual representation by both general and specific categories.…”

Section: Fam-based New Algorithmsmentioning

confidence: 99%

Advanced Developments and Applications of the Fuzzy ARTMAP Neural Network in Pattern Classification

Lerner

Guterman

Studies in Computational Intelligence

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Abstract. Since its inception in 1992, the fuzzy ARTMAP (FAM) neural network (NN) has attracted researchers' attention as a fast, accurate, off and online pattern classifier. Since then, many studies have explored different issues concerning FAM optimization, training and evaluation, e.g., model sensitivity to parameters, ordering strategy for the presentation of the training patterns, training method and method of predicting the classification accuracy. Other studies have suggested variants to FAM to improve its generalization capability or overcome the prime limitation of the model, which is category proliferation (i.e., model complexity that increases with data complexity). Category proliferation is pronounced in problems that are noisy or contain a large degree of class statistical overlapping. In many investigations, FAM was improved by incorporating elements of optimization theory, Bayes' decision theory, evolutionary learning, and cluster analysis. Due to its appealing characteristics, FAM and its variants have been applied extensively and successfully to real-world classification problems. Numerous applications were reported in, for example, the processing of signals from different sources, images, speech, and text; recognition of speakers, image objects, handwritten, and genetic abnormalities; and medical and fault diagnoses. When compared to other state-of-the-art machine learning classifiers, FAM and its variants showed superior speed and ease of training, and in most cases they delivered comparable classification accuracy.

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Generalization, discrimination, and multiple categorization using adaptive resonance theory

Cited by 14 publications

References 18 publications

Motif Discoveries in Unaligned Molecular Sequences using Self-Organizing Neural Networks

Motif Discoveries in Unaligned Molecular Sequences using Self-Organizing Neural Networks

Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence

Advanced Developments and Applications of the Fuzzy ARTMAP Neural Network in Pattern Classification

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