Summary: 166 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 11, NO. 1, JANUARY/FEBRUARY 1999
Rule-Induction and Case-Based Reasoning:
Hybrid Architectures Appear Advantageous
Nick Cercone, Senior Member, IEEE, Aijun An, and Christine Chan
Abstract--Researchers have embraced a variety of machine learning (ML) techniques in their efforts to improve the quality of
learning programs. The recent evolution of hybrid architectures for machine learning systems has resulted in several approaches
that combine rule-induction methods with case-based reasoning techniques to engender performance improvements over more-
traditional one-representation architectures. We briefly survey several major rule-induction and case-based reasoning ML systems.
We then examine some interesting hybrid combinations of these systems, and explain their strengths and weaknesses as learning
systems. We present a balanced approach to constructing a hybrid architecture, along with arguments in favor of this balance and
mechanisms for achieving a proper balance. Finally, we present some initial empirical results from testing our ideas and draw some
conclusions based on those results.
Index Terms--Case-based reasoning, rule induction, machine learning, classification, numeric prediction.
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ACHINE learning (ML) has evolved rapidly over the
past two decades. ML researchers have embraced
a variety of machine learning techniques in their efforts
to improve the quality of learning programs. The rela-
tively recent development of hybrid representations for ML