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- Title
Ratio Selection for Classification Models.
- Authors
Galvão, Roberto; Becerra, Victor; Abou-Seada, Magda
- Abstract
This paper is concerned with the selection of inputs for classification models based on ratios of measured quantities. For this purpose, all possible ratios are built from the quantities involved and variable selection techniques are used to choose a convenient subset of ratios. In this context, two selection techniques are proposed: one based on a pre-selection procedure and another based on a genetic algorithm. In an example involving the financial distress prediction of companies, the models obtained from ratios selected by the proposed techniques compare favorably to a model using ratios usually found in the financial distress literature.
- Subjects
DISCRIMINANT analysis; MULTIVARIATE analysis; GENETIC algorithms; FINANCIAL stress; RATIO analysis; MARKETING forecasting
- Publication
Data Mining & Knowledge Discovery, 2004, Vol 8, Issue 2, p151
- ISSN
1384-5810
- Publication type
Article
- DOI
10.1023/B:DAMI.0000015913.38787.b3