Paper Info
Title
The repository method for chance discovery in financial forecasting
Abstract
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.
Year
DOI
Venue
2006
10.1007/11893011_5
KES (3)
Keywords
Field
DocType
different way,genetic algorithms,genetic programming,negative case,positive case,future opportunity,positive instance,different rule,chance discovery,high chance,financial forecasting,high return,repository method,decision tree,genetic algorithm,machine learning
Decision tree,Computer science,Inversion (meteorology),Stock exchange,Exploit,Genetic programming,Artificial intelligence,Knowledge engineering,Financial market,Genetic algorithm,Machine learning,Distributed computing
Conference
Volume
ISSN
ISBN
4253
0302-9743
3-540-46542-1
Citations 
PageRank 
References 
4
0.48
6
Authors
2
Name
Order
Citations
PageRank
Alma Lilia Garcia-Almanza1292.56
Edward P. K. Tsang289987.77