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Title: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
Authors: Fayyad, U.
Irani, K.
Issue Date: 1-Sep-1993
Citation: Chambery, France
Abstract: Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuous-valued attribute into multiple intervals.
Appears in Collections:JPL TRS 1992+

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