Semantic Feature Construction

An effective set of features is integral to the success of machine learning algorithms. Semantic feature construction is the knowledge-driven manipulation of the propositional descriptor space of a set of examples for use in a learning algorithm. Two important sources of semanticsfor feature construction are the semantic type (and associated semantic properties) and the semantic class of features. These semantics canbe captured in a knowledge base and utilized to constrain search through the space of constructed features. This dissertation presents a systemthat captures semantic feature construction knowledge and implements a search algorithm that respects that knowledge. Results are presentedfor different combinations of features generated from different successor functions used in search. These results are compiled over many learning problems and several learning algorithms. Other results are also presentedfor different levels of detail in semantic knowledge. Generally, semantics are an effective guide in the space of constructed features

The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economics

. Constructive induction divides the problem of learning an inductive hypothesis into two intertwined searches: one---for the "best" representation space, and two---for the "best" hypothesis in that space. In datadriven constructive induction (DCI), a learning system searches for a better representation space by analyzing the input examples (data). The presented datadriven constructive induction method combines an AQ-type learning algorithm with two classes of representation space improvement operators: constructors, and destructors. The implemented system, AQ17-DCI, has been experimentally applied to a GNP prediction problem using a World Bank database. The results show that decision rules learned by AQ17-DCI outperformed the rules learned in the original representation space both in predictive accuracy and rule simplicity. 1 Introduction The basic premise of research on constructive induction (CI) is that results of a learning process directly depend on the quality of the represen..