Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming and emergent intelligence
Advances in genetic programming
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Software—Practice & Experience
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Preface: special issue on chance discovery
New Generation Computing - Special issue on chance discovery
Chance Discovery
EDDIE-automation, a decision support tool for financial forecasting
Decision Support Systems - Special issue: Data mining for financial decision making
Detection of stock price movements using chance discovery and genetic programming
International Journal of Knowledge-based and Intelligent Engineering Systems - Chance discovery
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The aim of this work is to forecast future opportunities in financial stock markets, in particular, we focus our attention on situations where positive instances are rare, which falls into the domain of Chance Discovery. Machine learning classifiers extend the past experiences into the future. However the imbalance between positive and negative cases poses a serious challenge to machine learning techniques. Because it favours negative classifications, which has a high chance of being correct due to the nature of the data. Genetic Algorithms have the ability to create multiple solutions for a single problem. To exploit this feature we propose to analyse the decision trees created by Genetic Programming. The objective is to extract and collect different rules that classify the positive cases. It lets model the rare instances in different ways, increasing the possibility of identifying similar cases in the future. To illustrate our approach, it was applied to predict investment opportunities with very high returns. From experiment results we showed that the Repository Method can consistently improve both the recall and the precision.