Genetic algorithm based wrapper feature selection on hybrid prediction model for analysis of high dimensional data

Anirudha, R C; Kannan, R. Jagadeesh; Patil, Nagamma

doi:10.1109/iciinfs.2014.7036522

Cited by 25 publications

(14 citation statements)

References 8 publications

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“…Several classification techniques have been applied on the PID dataset such as ANNs, Bayesian-based approaches, fuzzy logic, decision trees, K-means, Support Vector Machines, random forests (RF), Genetic algorithms, and K-Nearest Neighbors, for producing the risk of developing T2DM (Anirudha, Kannan, & Patil, 2014;Belciug & Gorunescu, 2014;Bioch, Meer, & Potharst, 1996;Carpenter & Markuzon, 1998;Gürbüz & Kiliç, 2014;Ilango & Ramaraj, 2010;Kahramanli & Allahverdi, 2008;Michie, Spiegelhalter, & Taylor, 1994;Nanni, Fantozzi, & Lazzarini, 2015;Patil, Joshi, & Toshniwal, 2010;Perez, Yanez-Marquez, Camacho-Nieto, Lopez-Yanez, & Arguelles-Cruz, 2015;Purwar & Singh, 2015;Seera & Lim, 2014;Sutanto & Ghani, 2015;Yilmaz, Inan, & Uzer, 2014;Zhu, Xie, & Zheng, 2015). In order to provide evidence of advancing the current state of the art, the performance of the proposed models has been comparatively assessed with those obtained by applying logistic regression, Bayesian-based approaches, and decision trees.…”

Section: Introductionmentioning

confidence: 99%

Comparative assessment of statistical and machine learning techniques towards estimating the risk of developing type 2 diabetes and cardiovascular complications

et al. 2017

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The aim of the present study is to comparatively assess the performance of different machine learning and statistical techniques with regard to their ability to estimate the risk of developing type 2 diabetes mellitus (Case 1) and cardiovascular disease complications (Case 2). This is the first work investigating the application of ensembles of artificial neural networks (EANN) towards producing the 5‐year risk of developing type 2 diabetes mellitus and cardiovascular disease as a long‐term diabetes complication. The performance of the proposed models has been comparatively assessed with the performance obtained by applying logistic regression, Bayesian‐based approaches, and decision trees. The models' discrimination and calibration have been evaluated using the classification accuracy (ACC), the area under the curve (AUC) criterion, and the Hosmer–Lemeshow goodness of fit test. The obtained results demonstrate the superiority of the proposed models (EANN) over the other models. In Case 1, EANN with different topologies has achieved high discrimination and good calibration performance (ACC = 80.20%, AUC = 0.849, p value = .886). In Case 2, EANN based on bagging has resulted in good discrimination and calibration performance (ACC = 92.86%, AUC = 0.739, p value = .755).

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Section: Introductionmentioning

confidence: 99%

Comparative assessment of statistical and machine learning techniques towards estimating the risk of developing type 2 diabetes and cardiovascular complications

et al. 2017

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“…The use of evolutionary strategies for the selection of features has been initially proposed in [19]. Since then, it has been regarded as a powerful tool for feature selection in machine learning [17] and proposed by numerous authors as a search strategy (see, e.g., [20], [21]). Multi-objective evolutionary algorithms are designed to solve a set of minimization/maximization problems for a tuple of n functions…”

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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“…The use of evolutionary strategies for the selection of features has been initially proposed in Siedlecki and Sklansky (). Since then, it has been regarded as a powerful tool for feature selection in machine learning (Vafaie & Jong, ) and proposed by numerous authors as a search strategy (see, e.g., (ElAlami, ; Anirudha, Kannan, & Patil, )). Multi‐objective evolutionary algorithms are designed to solve a set of minimization/maximization problems for a tuple of n functions

f_{1} ((),, \overset{true\to}{x}), italic\dots, f_{n} ((),, \overset{true\to}{x})

, where

\overset{x}{true}

is a vector of parameters belonging to a given domain.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised feature selection for interpretable classification in behavioral assessment of children

et al. 2016

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In this paper, we consider a data set taken from the administration of the Behavior Assessment\ud System for Children test to 157 subjects, and we approach the problem of clustering and classify\ud the subjects in an interpretable fashion. Because the Behavior Assessment System for Children test\ud is originally composed of 149 questions (152 in the particular version used for this experiment), we\ud first propose a feature selection wrapper model composed by a multi‐objective evolutionary\ud algorithm, the iterative clustering method expectation–maximization, and the classifier C4.5 for\ud the unsupervised feature selection towards the classification of the data with two objectives:\ud maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier.\ud We propose a methodology to integrate feature selection for unsupervised classification,\ud model evaluation, decision‐making (to choose the most satisfactory model according to an a\ud posteriori process in a multi‐objective context), and testing. The selected data set that is the result\ud of this process, where each instance is labeled with its class, is then used for supervised learning via\ud both C4.5 and a novel evolutionary computation‐based fuzzy classifier to obtain interpretable rules.\ud We discuss and compare the behavior of two different evolutionary algorithms (ENORA (Evolutionary\ud NOn‐dominated Radial slots based Algorithm) and NSGA‐II (Non‐dominated Sorted\ud Genetic Algorithm)) at different levels: as search strategies for feature selection, as search strategies\ud for fuzzy classification, and in terms of quality of the results. It turns out that ENORA behaves\ud better in terms of quality of the result in the feature selection phase (obtaining a selection that\ud shows higher accuracy under C4.5 after cross‐validation), and again in the fuzzy classification\ud phase, from both points of view: hypervolume evolution and interpretability of results. During\ud the entire process, the solutions are validated by the psychologists who collected the data

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Genetic algorithm based wrapper feature selection on hybrid prediction model for analysis of high dimensional data

Cited by 25 publications

References 8 publications

Comparative assessment of statistical and machine learning techniques towards estimating the risk of developing type 2 diabetes and cardiovascular complications

Comparative assessment of statistical and machine learning techniques towards estimating the risk of developing type 2 diabetes and cardiovascular complications

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

Unsupervised feature selection for interpretable classification in behavioral assessment of children

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