Experiments with Incremental Concept Formation: UNIMEM
Machine LearningPublished 1 September 1987Open access
Michael Lebowitz
Citations289
SJR quartileQ1
SJR score1.15
SNIP2.14
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TL;DR
UNIMEM is a robust program that can be run on many domains with real-world problem characteristics such as uncertainty, incompleteness, and large numbers of examples, including the automatic creation of non-disjoint concept hierarchies that are evaluated over time.
Abstract
Learning by observation involves automatic creation of categories that summarize experience. In this paper we present UNIMEM, an artificial intelligence system that learns by observation. UNIMEM is a robust program that can be run on many domains with real-world problem characteristics such as uncertainty, incompleteness, and large numbers of examples. We give an overview of the program that illustrates several key elements, including the automatic creation of non-disjoint concept hierarchies that are evaluated over time. We then describe several experiments that we have carried out with UNIMEM, including tests on different domains (universities, Congressional voting records, and terrorist events) and an examination of the effect of varying UNIMEM's parameters on the resulting concept hierarchies. Finally we discuss future directions for our work with the program.
Keywords
Computer Science
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