Inductive concept learning in the absence of labeled counter-examples

A. Skabar, K. Biswas, Binh Pham, A. Maeder

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Supervised machine learning techniques generally require that the training set on which learning is based contains sufficient examples representative of the target concept, as well as known counter-examples of the concept. However in many application domains it is not possible to supply a set of labeled counter-examples. This paper presents a technique that combines supervised and unsupervised learning to discover symbolic concept descriptions from a training set in which only positive instances appear with class labels. Experimental results obtained from applying the technique to several real world datasets are provided. These results suggest that in some problems domain learning without labeled counter-examples can lead to classification performance comparable to that of conventional learning algorithms, despite the fact that the latter use additional class information. The technique is able to cope with noise in the training set, and is applicable to a broad range of classification and pattern recognition problems.

Original languageEnglish
Title of host publication23rd Australasian Computer Science Conference, ACSC 2000 : 31 January-3 February 2000, Canberra, Australia
Subtitle of host publicationproceedings
EditorsJenny Edwards
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)076950518X, 9780769505183
Publication statusPublished - Jan 2000
Externally publishedYes
Event23rd Australasian Computer Science Conference, ACSC 2000 - Canberra, Australia
Duration: 31 Jan 20003 Feb 2000

Publication series

NameProceedings - 23rd Australasian Computer Science Conference, ACSC 2000


Conference23rd Australasian Computer Science Conference, ACSC 2000


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