"formal concept analysis; matrix decomposition; decision trees; machine learning; data preprocessing"@en . "Sevilla" . "1"^^ . . "Preprocessing input data for machine learning by FCA" . "1"^^ . "2010-10-19+02:00"^^ . . . . "978-84-614-4027-6" . "University of Sevilla" . "Preprocessing input data for machine learning by FCA"@en . . "[D999576B1478]" . . . . "12"^^ . "RIV/61989592:15310/10:10216519!RIV11-GA0-15310___" . "Preprocessing input data for machine learning by FCA" . . . "RIV/61989592:15310/10:10216519" . "OUTRATA, Jan" . . "P(GPP202/10/P360)" . . "Sevilla, \u0160pan\u011Blsko" . . "Proceedings of the 7th International Conference on Concept Lattices and Their Applications" . . . . "Preprocessing input data for machine learning by FCA"@en . "The paper presents an utilization of formal concept analysis in input data preprocessing for machine learning. Two preprocessing methods are presented. The first one consists in extending the set of attributes describing objects in input data table by new attributes and the second one consists in replacing the attributes by new attributes. In both methods the new attributes are defined by certain formal concepts computed from input data table. Selected formal concepts are so-called factor concepts obtained by boolean factor analysis, recently described by FCA. The ML method used to demonstrate the ideas is decision tree induction. The experimental evaluation and comparison of performance of decision trees induced from original and preprocessed input data is performed with standard decision tree induction algorithms ID3 and C4.5 on several benchmark datasets." . "15310" . . "281422" . . "The paper presents an utilization of formal concept analysis in input data preprocessing for machine learning. Two preprocessing methods are presented. The first one consists in extending the set of attributes describing objects in input data table by new attributes and the second one consists in replacing the attributes by new attributes. In both methods the new attributes are defined by certain formal concepts computed from input data table. Selected formal concepts are so-called factor concepts obtained by boolean factor analysis, recently described by FCA. The ML method used to demonstrate the ideas is decision tree induction. The experimental evaluation and comparison of performance of decision trees induced from original and preprocessed input data is performed with standard decision tree induction algorithms ID3 and C4.5 on several benchmark datasets."@en .