The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Forecasting S&P 500 stock index futures with a hybrid AI system
Decision Support Systems
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Feature selection in unsupervised learning via evolutionary search
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Subset Selection and Order Identification for Unsupervised Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
FS_SFS: A novel feature selection method for support vector machines
Pattern Recognition
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
A TSK type fuzzy rule based system for stock price prediction
Expert Systems with Applications: An International Journal
Application of wrapper approach and composite classifier to the stock trend prediction
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
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
Lessons in neural network training: overfitting may be harder than expected
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Stock trend prediction based on fractal feature selection and support vector machine
Expert Systems with Applications: An International Journal
Predicting high-tech equipment fabrication cost with a novel evolutionary SVM inference model
Expert Systems with Applications: An International Journal
Aseismic ability estimation of school building using predictive data mining models
Expert Systems with Applications: An International Journal
A hybrid stock selection model using genetic algorithms and support vector regression
Applied Soft Computing
Comparison of multilabel classification models to forecast project dispute resolutions
Expert Systems with Applications: An International Journal
Feature selection for classification of oscillating time series
Expert Systems: The Journal of Knowledge Engineering
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In this paper, we developed a prediction model based on support vector machine (SVM) with a hybrid feature selection method to predict the trend of stock markets. This proposed hybrid feature selection method, named F-score and Supported Sequential Forward Search (F_SSFS), combines the advantages of filter methods and wrapper methods to select the optimal feature subset from original feature set. To evaluate the prediction accuracy of this SVM-based model combined with F_SSFS, we compare its performance with back-propagation neural network (BPNN) along with three commonly used feature selection methods including Information gain, Symmetrical uncertainty, and Correlation-based feature selection via paired t-test. The grid-search technique using 5-fold cross-validation is used to find out the best parameter value of kernel function of SVM. In this study, we show that SVM outperforms BPN to the problem of stock trend prediction. In addition, our experimental results show that the proposed SVM-based model combined with F_SSFS has the highest level of accuracies and generalization performance in comparison with the other three feature selection methods. With these results, we claim that SVM combined with F_SSFS can serve as a promising addition to the existing stock trend prediction methods.