Caller behaviour classification using computational intelligence methods.
A classification system that accurately categorizes caller interaction within Interactive Voice Response systems is essential in determining caller behaviour. Field and call performance classifier for pay beneficiary application are developed. Genetic Algorithms, Multi-Layer Perceptron neural network, Radial Basis Function neural network, Fuzzy Inference Systems and Support Vector Machine computational intelligent techniques were considered in this research. Exceptional results were achieved. Classifiers with accuracy values greater than 90% were developed. The preferred models for field 'Say amount', 'Say confirmation' and call performance classification are the ensemble of classifiers. However, the Multi-Layer Perceptron classifiers performed the best in field 'Say account' and 'Select beneficiary' classification.
Patel
Pretesh B
PB
Faculty of Engineering and the Built Environment, University of Johannesburg, P O Box 524, Auckland Park, 2006, Johannesburg, South Africa. p.patel@uj.ac.za
Marwala
Tshilidzi
T
D000465:Algorithms; D001185:Artificial Intelligence; D003142:Communication; D017143:Fuzzy Logic; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D010465:Perception; D014831:Voice