Tree structured classifiers, interconnected data, and predictive accuracy

Article type: Research Article

Authors: Bursteinas, Borisas | Long, J.A.

Affiliations: SCISM South Bank University, London, SE1 OAA, UK. E-mail: borka@tdd.lt, longjaa@sbu.ac.uk

Abstract: Tree-structured classifiers have proved their ability to show good result in comparison with other classification techniques applied to real-world data which is usually noisy and uncertain. The purpose of this article is to survey a representative selection of existing types of tree-structured classifiers and evaluate their abilities to classify data sets with and without highly correlated attributes. The primary focus, however, is on identifying the suitability of applying tree-structured algorithms to data with interconnected attributes which is an essential feature of financial and business data. To carry out this study two financial data sets are used. The first data set contains quantitative data relating to a company's credit rating score. The second data set contains financial ratios related to company solvency. To determine the efficiency of different tree-structured algorithms five algorithms (four different types) were selected for comparison purposes. From the experimental results it is possible to see, that classification based on the mixed approach (NBTree) performed the best. Classifiers with a Bayesian approach also showed that they are stable.

Keywords: decision tree, tree-structured classifiers, Bayesian classifiers, C5.0, OC1, feature selection, correlated features

DOI: 10.3233/IDA-2000-4502

Journal: Intelligent Data Analysis, vol. 4, no. 5, pp. 397-410, 2000