Feature Selection for Natural Language Call Routing Based on Self-Adaptive Genetic Algorithm

Abstract

Abstract:The text classification problem for natural language call routing was considered in the paper. Seven different term weighting methods were applied. As dimensionality reduction methods, the feature selection based on self-adaptive GA is considered. k-NN, linear SVM and ANN were used as classification algorithms. The tasks of the research are the following: perform research of text classification for natural language call routing with different term weighting methods and classification algorithms and investigate the feature selection method based on self-adaptive GA. The numerical results showed that the most effective term weighting is TRR. The most effective classification algorithm is ANN. Feature selection with self-adaptive GA provides improvement of classification effectiveness and significant dimensionality reduction with all term weighting methods and with all classification algorithms. IntroductionNatural language call routing is an important problem in the design of modern automatic call services and the solving of this problem could lead to improvement of the call service [21]. Generally natural language call routing can be considered as two different problems. The first one is speech recognition of calls and the second one is topic categorization of users utterances for further routing. Topic categorization of users utterances can be also useful for multidomain spoken dialogue system design [12]. In this work we treat call routing as an example of a text classification application.In the vector space model [16] text classification is considered as a machine learning problem. The complexity of text categorization with a vector space model is compounded by the need to extract the numerical data from text information before applying machine learning algorithms. Therefore, text classification consists of two parts: text preprocessing and classification algorithm application using the obtained numerical data. Text preprocessing comprises three stages:-Textual feature extraction.-Term weighting -Dimensionality reduction. The first one is the textual feature extraction based on raw preprocessing of the documents. This process includes deleting punctuation, transforming capital letters to lowercase, and additional procedures such as stop-words filtering [4] and stemming [14]. Stop-words list contains pronouns, prepositions, articles and other words that usually have no importance for the classification. Using stemming it is possible to join different forms of the same word into one textual feature.The second stage is the numerical feature extraction based on term weighting. For term weighting we use "bag-of-words" model, in which the word order is ignored. There exist different unsupervised and supervised term weighting methods. The most well-known unsupervised term weighting method is TFIDF [15]. The following supervised term weighting methods are also considered in the paper:

Self-Tune Linear Adaptive-Genetic Algorithm for Feature Selection

“…This section describes the prediction outcome for four machinery datasets. The result tabulation begins with a classifier prediction performance review when subjected to multiclass classification datasets, according to Table 11,12,and 13. In general, the objective of a classifier is to obtain optimal prediction accuracy by minimising undesirable prediction error.…”

“…In cross-validation, the loss function kfoldLoss (13) is applied to calibrate the average misclassification proportion of testing-fold target output. The robustness of a cross-validated classifier is inversely proportional to mis kfold (13) The prediction outcome for 10 simulation cycles-including the number of feature subset, computation time, prediction accuracy, confusion matrix statistical analysis, and classification loss for the ten-fold cross-validated classifier-are tabulated in the following section. The simulation is performed using the 2019a version of Matlab software as the Integrated Development Environment (IDE).…”

“…The co-evolutionary technique manages the selection probabilities of a group of operators in a stepwise design until no changes arise (null operation). In addition, the Self-Configuring GA (SCGA) manipulates the probability value for genetic operators concurrently with reference to fitness function [13]. The Intuitionistic Fuzzy Logic (IFL) has been applied to the Multi-population GA (MpGA) in a top-down fashion in order to cross-validate the effectiveness of the genetic operators' sequence shuffling [14].…”

Context Analysis of Customer Requests using a Hybrid Adaptive Neuro Fuzzy Inference System and Hidden Markov Models in the Natural Language Call Routing Problem

“…They found that feature selection with selfadaptive GA provides improvement of classi cation e ectiveness and signi cant dimensionality reduction with all term weighting methods and with all classi cation algorithms [24].…”