An Approach to Concept Formation Based on Formal Concept Analysis
Summary :
Computational approaches to concept formation often share a top-down, incremental, hill-climbing classification, and differ from each other in the concept representation and quality criteria. Each of them captures part of the rich variety of conceptual knowledge and many are well suited only when the object-attribute distribution is not sparse. Formal concept analysis is a set-theoretic model that mathematically formulates the human understanding of concepts, and investigates the algebraic structure, Galois lattice, of possible concepts in a given domain. Adopting the idea of representing concepts by mutual closed sets of objects and attributes as well as the Galois lattice structure for concepts from formal concept analysis, we propose an approach to concept formation and develop OSHAM, a method that forms concept hierarchies with high utility score, clear semantics and effective even with sparse object-attribute distributions. In this paper we describe OSHAM, and in an attempt to show its performance we present experimental studies on a number of data sets from the machine learning literature.
- Publication
- IEICE TRANSACTIONS on Information Vol.E78-D No.5 pp.553-559
- Publication Date
- 1995/05/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Issue on Algorithmic Learning Theory)
- Category
- Machine Learning and Its Applications
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Tu Bao HO, "An Approach to Concept Formation Based on Formal Concept Analysis" in IEICE TRANSACTIONS on Information,
vol. E78-D, no. 5, pp. 553-559, May 1995, doi: .
Abstract: Computational approaches to concept formation often share a top-down, incremental, hill-climbing classification, and differ from each other in the concept representation and quality criteria. Each of them captures part of the rich variety of conceptual knowledge and many are well suited only when the object-attribute distribution is not sparse. Formal concept analysis is a set-theoretic model that mathematically formulates the human understanding of concepts, and investigates the algebraic structure, Galois lattice, of possible concepts in a given domain. Adopting the idea of representing concepts by mutual closed sets of objects and attributes as well as the Galois lattice structure for concepts from formal concept analysis, we propose an approach to concept formation and develop OSHAM, a method that forms concept hierarchies with high utility score, clear semantics and effective even with sparse object-attribute distributions. In this paper we describe OSHAM, and in an attempt to show its performance we present experimental studies on a number of data sets from the machine learning literature.
URL: https://global.ieice.org/en_transactions/information/10.1587/e78-d_5_553/_p
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@ARTICLE{e78-d_5_553,
author={Tu Bao HO, },
journal={IEICE TRANSACTIONS on Information},
title={An Approach to Concept Formation Based on Formal Concept Analysis},
year={1995},
volume={E78-D},
number={5},
pages={553-559},
abstract={Computational approaches to concept formation often share a top-down, incremental, hill-climbing classification, and differ from each other in the concept representation and quality criteria. Each of them captures part of the rich variety of conceptual knowledge and many are well suited only when the object-attribute distribution is not sparse. Formal concept analysis is a set-theoretic model that mathematically formulates the human understanding of concepts, and investigates the algebraic structure, Galois lattice, of possible concepts in a given domain. Adopting the idea of representing concepts by mutual closed sets of objects and attributes as well as the Galois lattice structure for concepts from formal concept analysis, we propose an approach to concept formation and develop OSHAM, a method that forms concept hierarchies with high utility score, clear semantics and effective even with sparse object-attribute distributions. In this paper we describe OSHAM, and in an attempt to show its performance we present experimental studies on a number of data sets from the machine learning literature.},
keywords={},
doi={},
ISSN={},
month={May},}
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TY - JOUR
TI - An Approach to Concept Formation Based on Formal Concept Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 553
EP - 559
AU - Tu Bao HO
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E78-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 1995
AB - Computational approaches to concept formation often share a top-down, incremental, hill-climbing classification, and differ from each other in the concept representation and quality criteria. Each of them captures part of the rich variety of conceptual knowledge and many are well suited only when the object-attribute distribution is not sparse. Formal concept analysis is a set-theoretic model that mathematically formulates the human understanding of concepts, and investigates the algebraic structure, Galois lattice, of possible concepts in a given domain. Adopting the idea of representing concepts by mutual closed sets of objects and attributes as well as the Galois lattice structure for concepts from formal concept analysis, we propose an approach to concept formation and develop OSHAM, a method that forms concept hierarchies with high utility score, clear semantics and effective even with sparse object-attribute distributions. In this paper we describe OSHAM, and in an attempt to show its performance we present experimental studies on a number of data sets from the machine learning literature.
ER -
English
