TY - JOUR
T1 - Joint concept formation
AU - Liu, Huan
AU - Wen, Wilson X.
PY - 1994/3/1
Y1 - 1994/3/1
N2 - Many concept formation systems construct disjoint-concept trees. However, a priori imposed tree structures may restrict the application of these systems in some domains. A joint concept formation scheme is thus proposed, which learns from observation, and constructs acyclic directed concept graphs (trees are a special case). We show that the joint concept formation system can avoid or alleviate some problems the disjoint concept formation system would face, such as the unique winner and oscillation problems. We also demonstrate that a joint concept formation system is able to generate a concept tree if such a regularity is found among the data. The experimental results are consistent with the expectations that the joint system is a generalized version of the disjoint system and improves the learning performance. Joint concept formation extends the classic works, such as COBWEB and ARACHNE.
AB - Many concept formation systems construct disjoint-concept trees. However, a priori imposed tree structures may restrict the application of these systems in some domains. A joint concept formation scheme is thus proposed, which learns from observation, and constructs acyclic directed concept graphs (trees are a special case). We show that the joint concept formation system can avoid or alleviate some problems the disjoint concept formation system would face, such as the unique winner and oscillation problems. We also demonstrate that a joint concept formation system is able to generate a concept tree if such a regularity is found among the data. The experimental results are consistent with the expectations that the joint system is a generalized version of the disjoint system and improves the learning performance. Joint concept formation extends the classic works, such as COBWEB and ARACHNE.
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U2 - 10.1006/knac.1994.1004
DO - 10.1006/knac.1994.1004
M3 - Article
AN - SCOPUS:43949152396
SN - 1042-8143
VL - 6
SP - 75
EP - 87
JO - Knowledge Acquisition
JF - Knowledge Acquisition
IS - 1
ER -