Genetic algorithms with sharing for multimodal function optimization
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Adaptation in natural and artificial systems
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A Knowledge-Intensive Genetic Algorithm for Supervised Learning
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Machine learning, neural and statistical classification
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Inductive Policy: The Pragmatics of Bias Selection
Machine Learning - Special issue on bias evaluation and selection
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Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
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An Investigation of Niche and Species Formation in Genetic Function Optimization
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A Comparison of Parallel and Sequential Niching Methods
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Inductive Strengthening: the Effects of a Simple Heuristic for Restricting Hypothesis Space Search
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A survey of evolutionary algorithms for data mining and knowledge discovery
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A hybrid decision tree/genetic algorithm method for data mining
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Self-organizing learning array and its application to economic and financial problems
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EuroGP'03 Proceedings of the 6th European conference on Genetic programming
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Genetic programming for attribute construction in data mining
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Agent-based distributed data mining: the KDEC scheme
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Expert Stock Picker: The Wisdom of (Experts in) Crowds
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Inducing decision trees with an ant colony optimization algorithm
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Inferring ECA-based rules for ambient intelligence using evolutionary feature extraction
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Prediction in financial domains is notoriously difficult for a number of reasons. First, theories tend to be weak or non-existent, which makes problem formulation open ended by forcing us to consider a large number of independent variables and thereby increasing the dimensionality of the search space. Second, the weak relationships among variables tend to be nonlinear, and may hold only in limited areas of the search space. Third, in financial practice, where analysts conduct extensive manual analysis of historically well performing indicators, a key is to find the hidden interactions among variables that perform well in combination. Unfortunately, these are exactly the patterns that the greedy search biases incorporated by many standard rule learning algorithms will miss. In this paper, we describe and evaluate several variations of a new genetic learning algorithm (GLOWER) on a variety of data sets. The design of GLOWER has been motivated by financial prediction problems, but incorporates successful ideas from tree induction and rule learning. We examine the performance of several GLOWER variants on two UCI data sets as well as on a standard financial prediction problem (S&P500 stock returns), using the results to identify one of the better variants for further comparisons. We introduce a new (to KDD) financial prediction problem (predicting positive and negative earnings surprises), and experiment with GLOWER, contrasting it with tree- and rule-induction approaches. Our results are encouraging, showing that GLOWER has the ability to uncover effective patterns for difficult problems that have weak structure and significant nonlinearities.