Feature selection using Sequential Forward Selection and classification applying Artificial Metaplasticity Neural Network

Marcano-Cedeño, Alexis; Quintanilla-Domínguez, J.; Cortina-Januchs, M. G.; Andina, Diego

doi:10.1109/iecon.2010.5675075

Cited by 148 publications

(79 citation statements)

References 19 publications

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“…In the final step, the chosen subset is validated using domain knowledge or a validation set. Sequential and exponential search methods for subset generation include [5], [6], [14], [15]. Instead, we propose the use of a random search method, as in [16], [17], [18], and, in particular, of a multi-objective evolutionary algorithm.…”

Section: Introductionmentioning

confidence: 99%

Attribute Selection Via Multi-Objective Evolutionary Computation Applied to Multi-Skill Contact Center Data Classification

Jiménez

Marzano²,

Sánchez

et al. 2015

2015 IEEE Symposium Series on Computational Intelligence

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Abstract-Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We propose the application of the multi-objective evolutionary algorithm ENORA to the task of feature selection for multi-class classification of data extracted from an integrated multi-channel multi-skill contact center, which include technical, service and central data for each session. Additionally, we propose a methodology to integrate feature selection for classification, model evaluation, and decision making to choose the most satisfactory model according to a a posteriori process in a multi-objective context. We check out our results by comparing the performance and the classification rate against the well-known multi-objective evolutionary algorithm NSGA-II. Finally, the best obtained solution is validated by a data expert's semantic interpretation of the classifier.

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Section: Introductionmentioning

confidence: 99%

Attribute Selection Via Multi-Objective Evolutionary Computation Applied to Multi-Skill Contact Center Data Classification

Jiménez

Marzano²,

Sánchez

et al. 2015

2015 IEEE Symposium Series on Computational Intelligence

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“…By selecting the best subsets of features, the accuracy of the overall system may increase (see e.g. (Marcano-Cede ando et al, 2010)). Finally, we plan to use compression methods like PC A and GPLV M to maximize the overall accuracy similar to what is described in (Zhong et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

Integrating Kinect Depth Data with a Stochastic Object Classification Framework for Forestry Robots

Pordel

Hellström

Ostovar

2012

Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics

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Abstract:In this paper we study the integration of a depth sensor and an RG B camera for a stochastic classification system for forestry robots. The images are classified as bush, tree, stone and human and are expected to come from a robot working in forest environment. A set of features is extracted from labeled images to train a number of stochastic classifiers. The outputs of the classifiers are then combined in a meta-classifier to produce the final result. The results show that using depth information in addition to the RG B results in higher classification performance.

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“…The feature selection process looks for and selects the optimal subset of feature that can effect on the whole contents of the dataset with the minimum error and information loss [13]. Feature selection approaches can be classified into three major classes based on the method of the search and selection method: complete, stochasticand heuristic search [14].…”

Section: Feature Selectionmentioning

confidence: 99%

“…In [14], a multilayer Feed Forward Neural Network (FFNN) affords the criterion of selection. The suggested method can provide effective outcomes in terms of search pace and accuracy when working with the classification technique.…”

Section: Feature Selectionmentioning

confidence: 99%

A new metaheuristic feature subset selection approach for image steganalysis

Mohammadi

Abadeh

2014

Journal of Intelligent &Amp; Fuzzy Systems

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Processing a huge amount of information takes extensive execution time and computational sources most of the time with low classification accuracy. As a result, it is needed to employ a phase of pre-analysis processing, which can influence the performance of execution time and computational sources and classification accuracy. One of the most important phases of preprocessing is Feature selection, which can improve the classification accuracy of steganalysis. The experiments are accomplished by using a large and important data set of 686 features vectores named SPAM. One of the promising application domains for such a feature selection process is steganalysis. In this paper, we propose a new metaheuristic approach for image steganalysis method for detecting stego images from the cover images in JPEG images using a feature selection technique based on an improved artificial bee colony. Within the ABC structure the k-Nearest Neighbor (kNN) method is employed for fitness evaluation. ABC and kNN have been adjusted together to make an operative dimension reduction method Experimental results demonstrate the effectiveness and accuracy of the proposed technique compared to recent ABC-based feature selection methods and other existing techniques.

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Feature selection using Sequential Forward Selection and classification applying Artificial Metaplasticity Neural Network

Cited by 148 publications

References 19 publications

Attribute Selection Via Multi-Objective Evolutionary Computation Applied to Multi-Skill Contact Center Data Classification

Attribute Selection Via Multi-Objective Evolutionary Computation Applied to Multi-Skill Contact Center Data Classification

Integrating Kinect Depth Data with a Stochastic Object Classification Framework for Forestry Robots

A new metaheuristic feature subset selection approach for image steganalysis

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