Boolean Feature Discovery in Empirical Learning
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Learning hard concepts through constructive induction: framework and rationale
Computational Intelligence
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Neural Networks
C4.5: programs for machine learning
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Machine Learning
Learning to learn
The anatomy of a large-scale hypertextual Web search engine
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Data mining: practical machine learning tools and techniques with Java implementations
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Machine Learning
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A perspective view and survey of meta-learning
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Link mining: a new data mining challenge
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Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
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This paper presents a genetic programming-based symbolic regression approach to the construction of relational features in link analysis applications. Specifically, we consider the problems of predicting, classifying and annotating friends relations in friends networks, based upon features constructed from network structure and user profile data. We first document a data model for the blog service LiveJournal, and define a set of machine learning problems such as predicting existing links and estimating inter-pair distance. Next, we explain how the problem of classifying a user pair in a social network, as directly connected or not, poses the problem of selecting and constructing relevant features. We use genetic programming to construct features, represented by multiple symbol trees with base features as their leaves. In this manner, the genetic program selects and constructs features that may not have been originally considered, but possess better predictive properties than the base features. Finally, we present classification results and compare these results with those of the control and similar approaches.