Logical Classification of Partially Ordered Data
This addresses a specific issue in machine learning for intelligent data analysis, but it appears incremental as it builds on classical logical classification concepts.
The paper tackles the problem of synthesizing correct supervised classification procedures by generalizing logical classification to handle partially ordered data, showing that learning such classifiers requires solving an intractable dualization problem. It demonstrates effectiveness on model and real-life data, though no concrete numbers are provided.
Issues concerning intelligent data analysis occurring in machine learning are investigated. A scheme for synthesizing correct supervised classification procedures is proposed. These procedures are focused on specifying partial order relations on sets of feature values; they are based on a generalization of the classical concepts of logical classification. It is shown that learning the correct logical classifier requires an intractable discrete problem to be solved. This is the dualization problem over products of partially ordered sets. The matrix formulation of this problem is given. The effectiveness of the proposed approach to the supervised classification problem is illustrated on model and real-life data.