Machine learning: paradigms and methods
Machine learning: paradigms and methods
Machine learning: an artificial intelligence approach volume III
Machine learning: an artificial intelligence approach volume III
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
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
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
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Genetic Algorithms and Genetic Programming in Computational Finance
Genetic Algorithms and Genetic Programming in Computational Finance
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Methodological Considerations on Chance Discovery
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Preface: special issue on chance discovery
New Generation Computing - Special issue on chance discovery
Intelligent data analysis
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
EDDIE-automation, a decision support tool for financial forecasting
Decision Support Systems - Special issue: Data mining for financial decision making
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Does cost-sensitive learning beat sampling for classifying rare classes?
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Computational Intelligence in Economics and Finance: Volume II
Computational Intelligence in Economics and Finance: Volume II
The repository method for chance discovery in financial forecasting
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Information Sciences: an International Journal
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The aim of this work is to detect important movements in financial stock prices that may indicate future opportunities or risks. The occurrence of such movements is scarce, thus this problem falls into the domain of Chance Discovery, a new research area whose objective is to identify rare events that may represent potential opportunities and risks. In this work we propose to capture patterns of the rare instances in different ways in order to increase the probability of identifying similar cases in the future. To generate more variety of solutions we evolve a genetic program, which is an evolutionary technique that is able to create multiple solutions for a single problem. The idea is to mine the knowledge acquired by the evolutionary process to extract and collect different rules that model the positive cases in several and novel ways. Once an important movement in financial markets has been discovered, human interaction is needed to analyze the markets conditions and determine if that movement could be a good opportunity to invest or could be the principle of a bubble or another critical event that represents a risk. Standard decision trees methods capture patterns from training data sets. However, when the chances are scare, some of the patters captured by the best rules may not repeat themselves in unseen cases. In this work we propose Repository Method which comprises multiple rules to form a more reliable classifier in rare cases. To illustrate our approach, it was applied to discover important movements in stock prices. From experimental results we showed that our approach can consistently detect rare cases in extreme imbalanced data sets.