Classification by feature partitioning
Machine Learning
Feature construction for game playing
Machines that learn to play games
Feature construction for reduction of tabular knowledge-based systems
Information Sciences—Informatics and Computer Science: An International Journal
A discriminant analysis using composite features for classification problems
Pattern Recognition
Expert Systems with Applications: An International Journal
Failure prediction in the Russian bank sector with logit and trait recognition models
Expert Systems with Applications: An International Journal
Diagnosis of gastric carcinoma by classification on feature projections
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Voting features based classifiers, shortly VFC, have been shown to perform well on most real-world data sets. They are robust to irrelevant features and missing feature values. In this paper, we introduce an extension to VFC, called voting features based classifier with feature construction, VFCC for short, and show its application to the problem of predicting if a bank will encounter financial distress, by analyzing current financial statements. The previously developed VFC learn a set of rules that contain a single condition based on a single feature in their antecedent. The VFCC algorithm proposed in this work, on the other hand, constructs rules whose antecedents may contain conjuncts based on several features. Experimental results on recent financial ratios of banks in Turkey show that the VFCC algorithm achieves better accuracy than other well-known rule learning classification algorithms.