The nature of statistical learning theory
The nature of statistical learning theory
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Using Cluster Similarity to Detect Natural Cluster Hierarchies
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Application of wrapper approach and composite classifier to the stock trend prediction
Expert Systems with Applications: An International Journal
A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting
Expert Systems with Applications: An International Journal
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
Using support vector machine with a hybrid feature selection method to the stock trend prediction
Expert Systems with Applications: An International Journal
On combining fractal dimension with GA for feature subset selecting
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Stock indices prediction using radial basis function neural network
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Spatial distance join based feature selection
Engineering Applications of Artificial Intelligence
Hi-index | 12.05 |
Stock trend prediction is regarded as a challenging task. Recently many researches have shown that a successful feature selection method can improve the prediction accuracy of stock market. This paper hybridizes fractal feature selection method and support vector machine to predict the direction of daily stock price index. Fractal feature selection method is suitable for solving the nonlinear problem and it can exactly spot how many important features we should choose. To evaluate the prediction accuracy of this method, this paper compares its performance with other five commonly used feature selection methods. The results show fractal feature selection method selects the relatively smaller number of features and it achieves the best average prediction accuracy.