Machine Learning methods and applications using Formal Concept Analysis
نویسنده
چکیده
Machine learning (ML) deals with algorithms that automatically improve with experience where the experience for a ML algorithm is huge repositories of data. Machine learning methods produce a program that fits data to a model from lots of examples that specify the correct output for a given input. Formal Concept Analysis (FCA) is a successful model of learning from positive and negative examples. FCA is a field of applied mathematics that aims to formalize the notions of a concept and a conceptual hierarchy by means of mathematical tools. It facilitates the use of mathematical reasoning for conceptual data analysis, knowledge representation and processing. FCA is a lattice-based classification method that can be considered as a symbolic data mining technique to be used for extracting (from a binary dataset) a set of concepts organized within a hierarchy (i.e. partial ordering). This paper presents a comprehensive literature survey on Machine Learning methods and applications using Formal Concept Analysis along with applications of FCA in the fields Ontology learning and mapping in Semantic web and Data Mining etc. Also it proposes a genetic algorithm based method for formal concept analysis.
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