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We propose aggregative context-aware fitness functions based on feature selection for evolutionary learning of characteristic graph patterns. The proposed fitness functions estimate the fitness of a set of correlated individuals rather than the sum of fitness of the individuals, and specify the fitness of an individual as its contribution degree in the context of the set. We apply the proposed fitness...
Knowledge acquisition from graph structured data is an important task in machine learning and data mining. Block preserving outerplanar graph patterns are graph structured patterns having structured variables and are suited to represent characteristic graph structures of graph data modeled as outerplanar graphs. We propose a learning method for acquiring characteristic multiple block preserving outerplanar...
Knowledge acquisition from graph structured data is an important task in machine learning and data mining. TTSP (Two-Terminal Series Parallel) graphs are used as data models for electric networks and scheduling. We propose a learning method for acquiring characteristic multiple graph structured patterns by evolutionary computation using sets of TTSP graph patterns as individuals, from positive and...
We consider evolutionary learning, based on Genetic Programming, for acquiring characteristic graph structures from positive and negative outerplanar graph data. We use block preserving outerplanar graph patterns as representations of graph structures. Block tree patterns are tree representations of block preserving outerplanar patterns, and have the structure of unrooted trees some of whose vertices...
Many chemical compounds can be expressed by a class of graphs called outerplanar graphs. By taking advantage of this tractable class of graphs, we use block preserving outerplanar graph patterns having structured variables for expressing structural features of outerplanar graphs. We propose a method for acquiring characteristic block preserving outerplanar graph patterns from positive and negative...
Machine learning and data mining from graph structured data have been studied intensively. Many chemical compounds can be expressed by outerplanar graphs. We use block preserving outerplanar graph patterns having structured variables for expressing structural features of outerplanar graphs. We propose a learning method for acquiring characteristic block preserving outerplanar graph patterns from positive...
Knowledge acquisition from tree structured data is an important task in machine learning and data mining. A tag tree pattern is a rooted tree structured pattern which has ordered children and structured variables representing arbitrary sub tree structures. In order to represent tree structured data about complex phenomena, we propose a learning method for acquiring characteristic multiple tree structured...
Knowledge acquisition from tree structured data is an important task in machine learning and data mining. We propose a learning method for acquiring characteristic sets of tree patterns with VLDC's from positive and negative tree structured data by using Genetic Programming and tree edit distance. We report experimental results on applying our method to glycan data.
Machine learning and data mining from graph structured data are studied intensively. TTSP (Two-Terminal Series Parallel) graphs are used as data models for electric networks and scheduling. A TTSP graph is a directed a cyclic graph obtained by recursively applying "series" and "parallel" operations. We propose an evolutionary method for acquiring characteristic TTSP graph patterns...
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