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.
|Number of pages||20|
|Journal||Data Mining and Knowledge Discovery|
|Publication status||Published - 2004|
- ratio selection, multivariate analysis, discriminant analysis, genetic, algorithm, distress prediction, finance, SUCCESSIVE PROJECTIONS ALGORITHM, MATRIX CONDITION NUMBER, MULTIVARIATE, CALIBRATION, WAVELENGTH SELECTION, MULTICOMPONENT ANALYSIS, BANKRUPTCY, PREDICTION, DISCRIMINANT-ANALYSIS, GENETIC ALGORITHMS, VARIABLE, SELECTION, FINANCIAL RATIOS