Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
Usama M. Fayyad, Keki B. Irani · International Joint Conference on Artificial Intelligence · 1993
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.