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Related Experiment Videos

Caller behaviour classification using computational intelligence methods.

Pretesh B Patel1, Tshilidzi Marwala

  • 1Faculty of Engineering and the Built Environment, University of Johannesburg, P O Box 524, Auckland Park, 2006, Johannesburg, South Africa. p.patel@uj.ac.za

International Journal of Neural Systems
|February 25, 2010
PubMed
Summary
This summary is machine-generated.

This study developed accurate classification systems for Interactive Voice Response (IVR) systems, achieving over 90% accuracy in categorizing caller behavior for beneficiary applications. Ensemble models excelled in most fields, with Multi-Layer Perceptron performing best for specific account and beneficiary selections.

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Area of Science:

  • Computational intelligence
  • Machine learning
  • Human-computer interaction

Background:

  • Accurate classification of caller interactions in Interactive Voice Response (IVR) systems is crucial for understanding caller behavior.
  • Developing effective classifiers for beneficiary applications requires robust computational techniques.
  • Existing methods may lack the precision needed for nuanced caller interaction analysis.

Purpose of the Study:

  • To develop and evaluate computational intelligence techniques for classifying caller interactions within IVR systems.
  • To create accurate field and call performance classifiers for a pay beneficiary application.
  • To identify the most effective machine learning models for specific classification tasks.

Main Methods:

  • Exploration of Genetic Algorithms, Multi-Layer Perceptron (MLP) neural networks, Radial Basis Function (RBF) neural networks, Fuzzy Inference Systems (FIS), and Support Vector Machines (SVM).
  • Development of classifiers for specific fields ('Say amount', 'Say confirmation', 'Say account', 'Select beneficiary') and overall call performance.
  • Comparative analysis of different computational intelligence techniques to determine optimal performance.

Main Results:

  • Achieved high accuracy values, exceeding 90%, for developed classifiers.
  • Ensemble of classifiers demonstrated superior performance for 'Say amount', 'Say confirmation', and call performance classification.
  • Multi-Layer Perceptron classifiers exhibited the best performance for 'Say account' and 'Select beneficiary' classification.

Conclusions:

  • Computational intelligence techniques can effectively classify caller interactions in IVR systems with high accuracy.
  • Ensemble models and MLP neural networks are highly suitable for specific classification tasks in beneficiary applications.
  • The developed classification systems provide valuable insights into caller behavior and can enhance IVR system performance.