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Author Valle, M.A.; Ruz, G.A.; Masias, V.H. pdf  doi
openurl 
  Title Using self-organizing maps to model turnover of sales agents in a call center Type
  Year 2017 Publication Applied Soft Computing Abbreviated Journal Appl. Soft. Comput.  
  Volume 60 Issue Pages 763-774  
  Keywords Self organizing map; Fused network; Classifier; Employee turnover; Call center; Personality traits  
  Abstract This paper proposes an approach for modeling employee turnover in a call center using the versatility of supervised self-organizing maps. Two main distinct problems exist for the modeling employee turnover: first, to predict the employee turnover at a given point in the sales agent's trial period, and second to analyze the turnover behavior under different performance scenarios by using psychometric information about the sales agents. Identifying subjects susceptible to not performing well early on, or identifying personality traits in an individual that does not fit with the work style is essential to the call center industry, particularly when this industry suffers from high employee turnover rates. Self-organizing maps can model non-linear relations between different attributes and ultimately find conditions between an individual's performance and personality attributes that make him more predisposed to not remain long in an organization. Unlike other models that only consider performance attributes, this work successfully uses psychometric information that describes a sales agent's personality, which enables a better performance in predicting turnover and analyzing potential personality profiles that can identify agents with better prospects of a successful career in a call center. The application of our model is illustrated and real data are analyzed from an outbound call center. (C) 2017 Elsevier B.V. All rights reserved.  
  Address [Valle, Mauricio A.] Univ Finis Terrae, Fac Econ & Negocios, Santiago, Chile, Email: mvalle@uft.cL  
  Corporate Author Thesis  
  Publisher (up) Elsevier Science Bv Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1568-4946 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000414072200057 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 795  
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Author Henriquez, P.A.; Ruz, G.A. pdf  doi
openurl 
  Title A non-iterative method for pruning hidden neurons in neural networks with random weights Type
  Year 2018 Publication Applied Soft Computing Abbreviated Journal Appl. Soft. Comput.  
  Volume 70 Issue Pages 1109-1121  
  Keywords Non -iterative learning; Neural networks; Random weights; Garson's algorithm; Pruning; Regression; Classification  
  Abstract Neural networks with random weights have the advantage of fast computational time in both training and testing. However, one of the main challenges of single layer feedforward neural networks is the selection of the optimal number of neurons in the hidden layer, since few/many neurons lead to problems of underfitting/overfitting. Adapting Garson's algorithm, this paper introduces a new efficient and fast non-iterative algorithm for the selection of neurons in the hidden layer for randomization based neural networks. The proposed approach is divided into three steps: (1) train the network with h hidden neurons, (2) apply Garson's algorithm to the matrix of the hidden layer, and (3) perform pruning reducing hidden layer neurons based on the harmonic mean. Our experiments in regression and classification problems confirmed that the combination of the pruning technique with these types of neural networks improved their predictive performance in terms of mean square error and accuracy. Additionally, we tested our proposed pruning method with neural networks trained under sequential learning algorithms, where Random Vector Functional Link obtained, in general, the best predictive performance compared to online sequential versions of extreme learning machines and single hidden layer neural network with random weights. (C) 2018 Elsevier B.V. All rights reserved.  
  Address [Henriquez, Pablo A.; Ruz, Gonzalo A.] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Ave Diagonal Las Torres 2640, Santiago, Chile, Email: pabhenriquez@alumnos.uai.cl;  
  Corporate Author Thesis  
  Publisher (up) Elsevier Science Bv Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1568-4946 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000443296000077 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 912  
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