ANS-based preprocessing of company performance indicators

Abstract

This work examines an artificial neural system (ANS) capable of dimensionality-reduction and its fitness to a business data analysis problem. While ANSs are often used in the later stages of explorative data analysis to place similar cases in clusters or to identify known patterns, the role of the ANS here is in the early pre-processing stage, to produce a smaller number of new variables which maintain important structures in the data. The ANS examined is a specially configured variation of the back-propagation multilayer perceptron (BP-MLP). The method is tested against others, on a supervised classification problem where the goal is to separate groups of similarly performing companies in the food and beverage sector of the Greek industry from their economic and non-economic characteristics. A typical pattern recognition algorithm is applied to classify the companies in performance groups, using a minimal pre-processing of the original company data b dimensionality-reduction through principal component analysis (PCA) c variables resulting from the examined ANS-based pre-processing method. The method’s ability to produce useful variables is evaluated by comparing the various groups of companies produced by the classifier algorithm using the three procedures.