Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Computer
The nature of statistical learning theory
The nature of statistical learning theory
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Hybrid neural network models for bankruptcy predictions
Decision Support Systems
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Algorithm 447: efficient algorithms for graph manipulation
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Expert Systems with Applications: An International Journal
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Forecasting financial condition of Chinese listed companies based on support vector machine
Expert Systems with Applications: An International Journal
A review of feature selection techniques in bioinformatics
Bioinformatics
Expert Systems with Applications: An International Journal
An integrative model with subject weight based on neural network learning for bankruptcy prediction
Expert Systems with Applications: An International Journal
Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors
Expert Systems with Applications: An International Journal
Ranking-order case-based reasoning for financial distress prediction
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Failure prediction of dotcom companies using hybrid intelligent techniques
Expert Systems with Applications: An International Journal
Financial distress prediction based on serial combination of multiple classifiers
Expert Systems with Applications: An International Journal
Predicting business failure using multiple case-based reasoning combined with support vector machine
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Expert Systems with Applications: An International Journal
Financial ratio selection for business crisis prediction
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Financially distressed prediction (FDP) has been a widely and continually studied topic in the field of corporate finance. One of the core problems to FDP is to design effective feature selection algorithms. In contrast to existing approaches, we propose an integrated approach to feature selection for the FDP problem that embeds expert knowledge with the wrapper method. The financial features are categorized into seven classes according to their financial semantics based on experts' domain knowledge surveyed from literature. We then apply the wrapper method to search for ''good'' feature subsets consisting of top candidates from each feature class. For concept verification, we compare several scholars' models as well as leading feature selection methods with the proposed method. Our empirical experiment indicates that the prediction model based on the feature set selected by the proposed method outperforms those models based on traditional feature selection methods in terms of prediction accuracy.