Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
The nature of statistical learning theory
The nature of statistical learning theory
Artificial Intelligence Review - Special issue on lazy learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Breeding Decision Trees Using Evolutionary Techniques
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
GA-facilitated classifier optimization with varying similarity measures
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Generation of comprehensible decision trees through evolution of training data
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Cancer Risk Analysis in Families With Hereditary Nonpolyposis Colorectal Cancer
IEEE Transactions on Information Technology in Biomedicine
On the mean accuracy of statistical pattern recognizers
IEEE Transactions on Information Theory
Using support vector machine with a hybrid feature selection method to the stock trend prediction
Expert Systems with Applications: An International Journal
OWA rough set model for forecasting the revenues growth rate of the electronic industry
Expert Systems with Applications: An International Journal
Integrating web mining and neural network for personalized e-commerce automatic service
Expert Systems with Applications: An International Journal
Designing simulated annealing and subtractive clustering based fuzzy classifier
Applied Soft Computing
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
Expert Systems with Applications: An International Journal
Improvements over adaptive local hyperplane to achieve better classification
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Save the best for last? The treatment of dominant predictors in financial forecasting
Expert Systems with Applications: An International Journal
Feature weighting by RELIEF based on local hyperplane approximation
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Stock price prediction based on a complex interrelation network of economic factors
Engineering Applications of Artificial Intelligence
| Hi-index | 12.06 |
The research on the stock market prediction has been more popular in recent years. Numerous researchers tried to predict the immediate future stock prices or indices based on technical indices with various mathematical models and machine learning techniques such as artificial neural networks (ANN), support vector machines (SVM) and ARIMA models. Although some researches in the literature exhibit satisfactory prediction achievement when the average percentage error and root mean square error are used as the performance metrics, the prediction accuracy of whether stock market goes or down is seldom analyzed. This paper employs wrapper approach to select the optimal feature subset from original feature set composed of 23 technical indices and then uses voting scheme that combines different classification algorithms to predict the trend in Korea and Taiwan stock markets. Experimental result shows that wrapper approach can achieve better performance than the commonly used feature filters, such as @g^2-Statistic, Information gain, ReliefF, Symmetrical uncertainty and CFS. Moreover, the proposed voting scheme outperforms single classifier such as SVM, kth nearest neighbor, back-propagation neural network, decision tree, and logistic regression.