Optimal Classification

Optimal classification refers to any method of classificationAlthough classification elements and attributes and decision actions and conditions may employ tables and trees there are important differences in how each is defined. A classification table or tree is defined by having an element with at least one attribute with at least one value and may have more than one element, attribute or attribute value but may not have more than one element per rule. On the other hand, a decision table or tree may have more than one condition per rule, more than one state per condition and more than one action per rule. , which inexclusively minimizes the number of queries necessary to identify a particular element within a bounded classA bounded class is a collection of elements defined by the same finite group of attributes.. Table of Contents 400x500px|right|Optimal Order Processing Rypka's Method Truth Table Size-Related Equations Separatory Equations Element-Related equations Characteristic-related equations Theoretical separation Empirical separation Target Set Truth Table Values Separation Stages Computational Example Application Example Notes Specific applications Classification of closed legal cases to help identify new ones Artificial Neural Networks Compute minimum number of hidden nodes Minimize training time Dynamic classification of publications Soil taxonomy See also Data mining Fuzzy logic Information retrieval External links LIBRARY OF CONGRESS CLASSIFICATION OUTLINE North American Industry Classification System (NAICS) 2000 Mathematics Subject Classification Standard Industrial Classification (SIC) System Search U.S. Patent Classification (USPC) System The Classification Society of North America (CSNA) "What is "entropy and information gain"?, in the context of decision trees?" Computer science O 25% __NOTOC__ __NOEDITSECTION__