An object-oriented implementation of an adaptive classification of job openings

Automating job classification is challenging because it involves a large number of dynamic classes and features, concept drift uncertainty, and noisy data. We present a software solution to this problem that consists of an incremental learning subsystem and a job classifier. We also describe our des...

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Bibliographic Details
Published inProceedings the 11th Conference on Artificial Intelligence for Applications pp. 9 - 16
Main Authors Clyde, S., Jianping Zhang, Chih-Chung Yao
Format Conference Proceeding
LanguageEnglish
Published IEEE Comput. Soc. Press 1995
Subjects
Computer errors
Database systems
Employment
Information systems
Job listing service
Job production systems
Learning systems
Modeling
Object oriented programming
Remuneration
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Summary:Automating job classification is challenging because it involves a large number of dynamic classes and features, concept drift uncertainty, and noisy data. We present a software solution to this problem that consists of an incremental learning subsystem and a job classifier. We also describe our design and implementation using object-oriented systems modeling, a complete object-oriented approach that supports analysis, specification and design, and has a smooth mapping to most oriented-object programming languages. Some experimental results and comparisons to other learning/classification algorithms are given. A production version of the software written in C/sup ++/ is performing with superior accuracy.< >
ISBN:0818670703
9780818670701
DOI:10.1109/CAIA.1995.378795