Attributive and Object Subcontexts in Inferring Good Maximally Redundant Tests
نویسندگان
چکیده
Inferring Good Maximally Redundant Classification Tests (GMRTs) as Formal Concepts is considered. Two kinds of classification subcontexts are defined: attributive and object ones. The rules of forming and reducing subcontexts based on the notion of essential attributes and objects are given. They lead to the possibility of the inferring control. In particular, an improved Algorithm for Searching all GMRTs on the basis of attributive subtask is proposed. The hybrid attributive and object approaches are presented. Some computational aspects of algorithms are analyzed.
منابع مشابه
Modeling Processes of Inferring Good Maximally Redundant Tests
Good test analysis is considered. Two kinds of classification subtasks are defined: attributive and object ones. Some ideas of modeling and optimization of inferring good maximally redundant tests are formalized. An algorithm of inferring good maximally redundant tests based on the decomposition into attributive subtasks is given, where good maximally redundant tests are regarded as concepts of...
متن کاملContext-Dependent Deductive and Inductive Reasoning Based on Good Diagnostic Tests
A sketch of classification reasoning is given in the paper. The key ideas of the reasoning are ideas of classification and its good approximations based on good diagnostic tests. Such good tests, which are maximally redundant (GMRTs), i.e. their subsets of attributes are closed, are considered. Classification reasoning embraces two interrelated processes: inductive inferring implicative asserti...
متن کاملInferring Minimal Rule Covers from Relations
An implication rule Q→R is roughly a statement of the form “for all objects in the database, if an object has Q then it has also R”. We introduce a definition of minimal cover for the set of implication rules that hold in a relation, by analogy with earlier work on functional dependencies, and present an approach to computing it. The core of the proposed approach is an algorithm for inferring a...
متن کاملModification of Good Tests in Dynamic Contexts: Application to Modeling Intellectual Development of Cadets
An approach to incremental learning of Good Maximally Redundant Diagnostic Tests (GMRTs) is considered. GMRT is a special formal concept in Formal Concept Analysis. Mining GMRTs from data is based on Galois lattice construction. Four situations of learning GMRTs are considered: inserting an object (value) and deleting an object (value). An application to modeling intellectual development of cad...
متن کاملNotes on Relation Between Symbolic Classifiers
Symbolic classifiers allow for solving classification task and provide the reason for the classifier decision. Such classifiers were studied by a large number of researchers and known under a number of names including tests, JSM-hypotheses, version spaces, emerging patterns, proper predictors of a target class, representative sets etc. Here we consider such classifiers with restriction on count...
متن کامل