Journal Article10.1023/A:1011219918340
Unsupervised Rough Set Classification Using GAs
Pawan Lingras
- 05 Oct 2001
- Vol. 16, Iss: 3, pp 215-228
108
TL;DR: This paper describes how genetic algorithms can be used to develop rough sets and the proposed rough set theoretic genetic encoding will be especially useful in unsupervised learning.
read more
Abstract: The rough set is a useful notion for the classification of objects when the available information is not adequate to represent classes using precise sets Rough sets have been successfully used in information systems for learning rules from an expert This paper describes how genetic algorithms can be used to develop rough sets The proposed rough set theoretic genetic encoding will be especially useful in unsupervised learning A rough set genome consists of upper and lower bounds for sets in a partition The partition may be as simple as the conventional expert class and its complement or a more general classification scheme The paper provides a complete description of design and implementation of rough set genomes The proposed design and implementation is used to provide an unsupervised rough set classification of highway sections
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Rudiments of rough sets
Zdziasław Pawlak,Andrzej Skowron +1 more
TL;DR: The basic concepts of rough set theory are presented and some rough set-based research directions and applications are pointed out, indicating that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences.
2.2K
Rough sets and Boolean reasoning
Zdzisław Pawlak,Andrzej Skowron +1 more
TL;DR: Methods based on the combination of rough sets and Boolean reasoning with applications in pattern recognition, machine learning, data mining and conflict analysis are discussed.
1K
Interval Set Clustering of Web Users with Rough K -Means
Pawan Lingras,Chad West +1 more
- 01 Jul 2004
TL;DR: A variation of the K-means clustering algorithm based on properties of rough sets is proposed, which represents clusters as interval or rough sets.
544
ECM: An evidential version of the fuzzy c-means algorithm
TL;DR: Experiments with synthetic and real data sets show that the proposed ECM (evidential c-means) algorithm can be considered as a promising tool in the field of exploratory statistics.
413
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
TL;DR: This article compares k-mean to fuzzy c-means and rough k-Means as important representatives of soft clustering, and surveys important extensions and derivatives of these algorithms.
192
References
•Book
Adaptation in natural and artificial systems
John H. Holland
- 01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
•Book
Genetic Algorithms + Data Structures = Evolution Programs
Zbigniew Michalewicz
- 01 Jan 1992
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
13.5K
•Journal Article
Maintaining knowledge about temporal intervals
TL;DR: An interval-based temporal logic is introduced, together with a computationally effective reasoning algorithm based on constraint propagation, which is notable in offering a delicate balance between space and time.
7.9K
Maintaining knowledge about temporal intervals
TL;DR: In this paper, an interval-based temporal logic is introduced, together with a computationally effective reasoning algorithm based on constraint propagation, which is notable in offering a delicate balance between time and space.
Rough sets
TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
7.2K