ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Discovery of Decision Rules by Matching New Objects Against Data Tables
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
RSES and RSESlib - A Collection of Tools for Rough Set Computations
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
Fundamenta Informaticae
ENDER: a statistical framework for boosting decision rules
Data Mining and Knowledge Discovery
Analogy-based reasoning in classifier construction
Transactions on Rough Sets IV
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
Fundamenta Informaticae
Lazy attribute selection: Choosing attributes at classification time
Intelligent Data Analysis
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We present an extension of the lazy rule induction algorithm from [1]. We extended it to deal with real-value attributes and generalised its conditions for symbolic non-ordered attributes. The conditions for symbolic attributes are defined by means of a metric over attribute domain. We show that commonly used rules are a special case of the proposed rules with a specific metric. We also relate the proposed algorithm to the discretisation problem. We illustrate that lazy approach can omit the discretisation time complexity.