Adaptive neuro fuzzy inference system, neural network and support vector machine for caller behavior classification

Pretesh B. Patel, Tshilidzi Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

A classification system that accurately categorizes caller behavior within Interactive Voice Response systems would assist in developing good automated self service applications. This paper details the implementation of such a classification system for a pay beneficiary application. Adaptive Neuro-Fuzzy Inference System (ANFIS), Feed forward Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers were created. Exceptional results were achieved. The ANN classifiers are the preferred models. ANN classifiers achieved 100% classification on 'Say account', 'Say amount' and 'Select beneficiary' unseen data. The ANN classifier yielded 95.42% accuracy on 'Say confirmation' unseen data.

Original languageEnglish
Title of host publicationProceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
Pages298-303
Number of pages6
DOIs
Publication statusPublished - 2011
Event10th International Conference on Machine Learning and Applications, ICMLA 2011 - Honolulu, HI, United States
Duration: 18 Dec 201121 Dec 2011

Publication series

NameProceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
Volume1

Conference

Conference10th International Conference on Machine Learning and Applications, ICMLA 2011
Country/TerritoryUnited States
CityHonolulu, HI
Period18/12/1121/12/11

Keywords

  • adaptive neuro-fuzzy inference system
  • artificial neural nerwork
  • classification
  • interactive voice response
  • support vector machine

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

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