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

Jiménez, Fernando; Marzano, Enrico; Sánchez, Gracia; Sciavicco, Guido; Vitacolonna, Nicola

doi:10.1109/ssci.2015.78

Cited by 16 publications

(21 citation statements)

References 23 publications

(31 reference statements)

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“…This paper extends the work reported in Jiménez et al (), which focuses on the application of attribute selection and supervised classification to phone session outcome prediction. A first, notable difference is given by the training data set in Jiménez et al (), only technical information was used as the basis for the prediction of outcomes, which are manually set by human operators, and, even though this led to some interesting insights on the domain, the results indicated the presence of a semantic gap between predictors and the predicted variable; on the contrary, here we look for relationships between the technical information of a call and the fact of it being managed or not, which is also an automatically recorded value.…”

Section: Introductionsupporting

confidence: 66%

“…Even though in ENORA, the individuals of each slot, when compared, may not be the best, this approach generates a better hypervolume than that of NSGA-II throughout the evolution process. This superiority of ENORA over NSGA-II has been verified in some feature selection problems for regression and classification tasks and for fuzzy classification inJiménez et al (2015),Jiménez et al (2017),and Jiménez et al (2014), respectively.…”

mentioning

confidence: 79%

“…The multiobjective evolutionary algorithms ENORA (Jiménez et al, ), ; strategy with μ = λ = popsize , where μ corresponds to the number of parents, and λ refers to the number of children (popsize is the population size), with binary tournament selection and a rank function based on Pareto fronts and crowding. The difference between ENORA and NSGA‐II is how the calculation of the ranking of the individuals in the population is performed.…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective evolutionary feature selection and fuzzy classification of contact centre data

Brunello

Jiménez

Marzano³

et al. 2019

Expert Systems

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In this work, a data set describing phone interactions arising in a multichannel and multiskill contact centre is considered with the aim of classifying inbound sessions into those that will be eventually managed by an agent and those that, instead, will be abandoned before. More precisely, the goal of the work is to extract interpretable pieces of information that allow us to predict whether a user will or will not abandon a call, which may turn out to be very useful for the purpose of contact centre managing. To this end, the performance of two well‐known, state‐of‐the‐art evolutionary algorithms for feature selection (evolutionary nondominated radial slots based algorithm and nondominated sorted genetic algorithm) is compared for the task of feature selection, under the criteria of accuracy and cardinality of the selection, as well as for the task of fuzzy rule extraction, under the criteria of interpretability, accuracy, and hypervolume test. The best obtained fuzzy classifier, chosen after a decision making process, is validated and interpreted by domain experts.

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Section: Introductionsupporting

confidence: 66%

mentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

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Multiobjective evolutionary feature selection and fuzzy classification of contact centre data

Brunello

Jiménez

Marzano³

et al. 2019

Expert Systems

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“…Moreover, there are several EA-related aspects that could be taken into account to enhance our results. First, the classical selection strategy implemented in NSGA-II has been improved in, for instance, the algorithm ENORA [21], [22]. Second, independently from the selection strategy, state-of-the-art implementations of EAs do not require explicit and fixed setting of the crossover and mutation rates, which are, instead, considered as characteristics of adaptiveness of each of the solutions.…”

Section: Discussionmentioning

confidence: 99%

Decision Tree Pruning via Multi-Objective Evolutionary Computation

Brunello¹,

Marzano²,

Montanari³

et al. 2017

IJMLC

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To date, decision trees are among the most used classification models. They owe their popularity to their efficiency during both the learning and the classification phases and, above all, to the high interpretability of the learned classifiers. This latter aspect is of primary importance in those domains in which understanding and validating the decision process is as important as the accuracy degree of the prediction. Pruning is a common technique used to reduce the size of decision trees, thus improving their interpretability and possibly reducing the risk of overfitting. In the present work, we investigate on the integration between evolutionary algorithms and decision tree pruning, presenting a decision tree post-pruning strategy based on the well-known multi-objective evolutionary algorithm NSGA-II. Our approach is compared with the default pruning strategies of the decision tree learners C4.5 (J48 -on which the proposed method is based) and C5.0. We empirically show that evolutionary algorithms can be profitably applied to the classical problem of decision tree pruning, as the proposed strategy is capable of generating a more variegate set of solutions than both J48 and C5.0; moreover, the trees produced by our method tend to be smaller than the best candidates produced by the classical tree learners, while preserving most of their accuracy and sometimes improving it.

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“…While NSGA-II is a standard multi-objective evolutionary algorithm (Deb, 2001), applying ENORA (Jiménez et al, 2002;Jiménez et al, 2014;Jiménez, Marzano, Sánchez, Sciavicco, & Vitacolonna, 2015) to feature selection and fuzzy classification is relatively new. Its main components, that is, selection and sampling mechanisms and generational replacement schemata are described in this section.…”

Section: The Multi-objective Evolutionary Algorithm Enoramentioning

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

Cited by 16 publications

References 23 publications

Multiobjective evolutionary feature selection and fuzzy classification of contact centre data

Multiobjective evolutionary feature selection and fuzzy classification of contact centre data

Decision Tree Pruning via Multi-Objective Evolutionary Computation

Unsupervised feature selection for interpretable classification in behavioral assessment of children

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