A machine learning approach to inductive query by examples: An experiment using relevance feedback, ID3, genetic algorithms, and simulated annealing

Recently, information science researchers have turned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance t...

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Bibliographic Details
Published inJournal of the American Society for Information Science and Technology Vol. 49; no. 8; p. 693
Main Authors Chen, Hsinchun, Shankaranarayanan, Ganesan, She, Linlin, Iyer, Anand
Format Journal Article
LanguageEnglish
Published Hoboken Wiley Periodicals Inc 01.06.1998
Subjects
Algorithms
Biological evolution
Computer simulation
Data processing
Evolutionary algorithms
Feedback
Genetic algorithms
Information processing
Information retrieval
Knowledge representation
Library science
Logic
Machine learning
Relevance feedback
Simulated annealing
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Summary:Recently, information science researchers have turned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems. An overview of these newer techniques and their use in information retrieval research is presented. Three promising methods are presented: the symbolic ID3 algorithm, evolution-based genetic algorithms, and simulated annealing. Their knowledge representations and algorithms in the unique context of information retrieval are discussed. An experiment using a 8000-record COMPEN database was performed to examine the performances of these inductive query-by-example techniques in comparison with the performance of the conventional relevance feedback method.
ISSN:2330-1635
2330-1643