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Author Salas, R.; Allende, H.; Moreno, S.; Saavedra, C. pdf  doi
openurl 
  Title Flexible Architecture of Self Organizing Maps for changing environments Type Journal Article
  Year 2005 Publication Lecture Notes in Computer Sciences Abbreviated Journal Lect. Notes Comput. Sc.  
  Volume 3773 Issue Pages 642-653  
  Keywords catastrophic interference; Artificial Neural Networks; Self Organizing Maps; pattern recognition  
  Abstract Catastrophic Interference is a well known problem of Artificial Neural Networks (ANN) learning algorithms where the ANN forget useful knowledge while learning from new data. Furthermore the structure of most neural models must be chosen in advance. In this paper we introduce a hybrid algorithm called Flexible Architecture of Self Organizing Maps (FASOM) that overcomes the Catastrophic Interference and preserves the topology of Clustered data in changing environments. The model consists in K receptive fields of self organizing maps. Each Receptive Field projects high-dimensional data of the input space onto a neuron position in a low-dimensional output space grid by dynamically adapting its structure to a specific region of the input space. Furthermore the FASOM model automatically finds the number of maps and prototypes needed to successfully adapt to the data. The model has the capability of both growing its structure when novel clusters appears and gradually forgets when the data volume is reduced in its receptive fields. Finally we show the capabilities of our model with experimental results using synthetic sequential data sets and real world data.  
  Address Univ Valparaiso, Dept Comp, Valparaiso, Chile, Email: rodrigo.salas@uv.cl  
  Corporate Author Thesis  
  Publisher Springer-Verlag Berlin Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN Medium  
  Area Expedition Conference Progress In Pattern Recognition  
  Notes WOS:000234341500067 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 44  
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Author Valle, M.A.; Ruz, G.A.; Masias, V.H. pdf  doi
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  Title Using self-organizing maps to model turnover of sales agents in a call center Type Journal Article
  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 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 no  
  Call Number UAI @ eduardo.moreno @ Serial 795  
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