Factorizing three-way ordinal data using triadic formal concepts
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
Study on data preprocessing for daylight climate data
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
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We present two input data preprocessing methods for machine learning (ML). 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. The methods utilize formal concept analysis (FCA) and boolean factor analysis, recently described by FCA, in that the new attributes are defined by so-called factor concepts computed from input data table. The methods are demonstrated on 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.