Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Cost-Sensitive Feature Reduction Applied to a Hybrid Genetic Algorithm
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
Noise Elimination in Inductive Concept Learning: A Case Study in Medical Diagnosois
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
An extended transformation approach to inductive logic programming
ACM Transactions on Computational Logic (TOCL) - Special issue devoted to Robert A. Kowalski
Propositionalization approaches to relational data mining
Relational Data Mining
Attribute generation based on association rules
Knowledge and Information Systems
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
A Data Pre-processing Method Using Association Rules of Attributes for Improving Decision Tree
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Transformation-Based Learning Using Multirelational Aggregation
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Induction of comprehensible models for gene expression datasets by subgroup discovery methodology
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Feature construction for reduction of tabular knowledge-based systems
Information Sciences—Informatics and Computer Science: An International Journal
Expert-guided subgroup discovery: methodology and application
Journal of Artificial Intelligence Research
Relevancy in constraint-based subgroup discovery
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
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Some machine learning algorithms enable the learner to extend its vocabulary with new terms if, for a given a set of training examples, the learner's vocabulary is too restricted for solving the learning task. In this article, the authors propose a filter that selects the potentially relevant terms from the set of constructed terms, and eliminates the terms which are irrelevant for the learning task. By biasing constructive induction (or predicate invention) to relevant terms only, the explored space of constructed terms can be much larger. The elimination of irrelevant terms is especially well suited for learners of large time or space complexity (such as genetic algorithms and neural nets). This article presents the Reduce algorithm for eliminating irrelevant terms and a case study in which the authors use Reduce to preprocess data for a hybrid genetic algorithm RL-ICET.