A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Ant algorithms for discrete optimization
Artificial Life
Intelligent Systems for Business: Expert Systems with Neural Networks
Intelligent Systems for Business: Expert Systems with Neural Networks
Proportional k-Interval Discretization for Naive-Bayes Classifiers
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Feature selection in scientific applications
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Support vector machines based on K-means clustering for real-time business intelligence systems
International Journal of Business Intelligence and Data Mining
International Journal of Electronic Finance
Mining in-depth patterns in stock market
International Journal of Intelligent Systems Technologies and Applications
A stochastic nature inspired metaheuristic for clustering analysis
International Journal of Business Intelligence and Data Mining
cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
A neural network with a case based dynamic window for stock trading prediction
Expert Systems with Applications: An International Journal
Review: A review of ant algorithms
Expert Systems with Applications: An International Journal
Forecasting stock market short-term trends using a neuro-fuzzy based methodology
Expert Systems with Applications: An International Journal
Ant colony and particle swarm optimization for financial classification problems
Expert Systems with Applications: An International Journal
An ACS-based framework for fuzzy data mining
Expert Systems with Applications: An International Journal
Hybrid approach for pole assignment using LQR technique and Ant System metaheuristic
International Journal of Artificial Intelligence and Soft Computing
Expert Systems with Applications: An International Journal
A genetic network programming with learning approach for enhanced stock trading model
Expert Systems with Applications: An International Journal
A novel ACO-GA hybrid algorithm for feature selection in protein function prediction
Expert Systems with Applications: An International Journal
On an ant colony-based approach for business fraud detection
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Two models of parallel ACO algorithms for the minimum tardy task problem
International Journal of High Performance Systems Architecture
A Stock Market Trend Prediction System Using a Hybrid Decision Tree-Neuro-Fuzzy System
ARTCOM '10 Proceedings of the 2010 International Conference on Advances in Recent Technologies in Communication and Computing
Stock Market Prediction Using a Hybrid Neuro-fuzzy System
ARTCOM '10 Proceedings of the 2010 International Conference on Advances in Recent Technologies in Communication and Computing
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Computational learning techniques for intraday FX trading using popular technical indicators
IEEE Transactions on Neural Networks
A Hybrid Neurogenetic Approach for Stock Forecasting
IEEE Transactions on Neural Networks
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Ant Colony Optimisation (ACO) algorithms use simple mutually cooperating agents (ants) to produce a robust and adaptive search system, which can be used for knowledge discovery. In this paper, a Support Vector Machine (SVM)-cAnt-Miner-based system for predicting the next-day's trend in stock markets is proposed. The trend predicted by the proposed system is then used to identify the appropriate time to buy and sell securities. Performance of the proposed system is evaluated against SVM-Ant-Miner, SVM-Ant-Miner2, Naïve-Bayes and an Artificial Neural Network (ANN)-based trend prediction system. The results indicate that the proposed system outperforms all the other techniques considered.