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Querying Biological Sequences Docking Using Different Constraint Programming's: a Survey
English
2015
International Journal of Computer Trends and Technology
English
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In mixed data extract the useful biological sequences data with subgroup discovery iterative genetic algorithm, Cluster based fuzzy genetic algorithm mining framework, hierarchical fuzzy rule based systems ...
Generally Fuzzy set theory handling the issues related understandability patterns, insufficient and noisy data. Fuzzy set theory provides the faster solutions. ...
Keywords: Fuzzy set theory, membership functions, fuzzy rules, KDD, learning techniques, genetic algorithm. ...
doi:10.14445/22312803/ijctt-v22p110
fatcat:ag36qn6m4jdkhlakxvrbbisyma
Induction of Fuzzy-Rule-Based Classifiers With Evolutionary Boosting Algorithms
2004
IEEE transactions on fuzzy systems
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Index Terms-Boosting algorithms, evolutionary algorithms, fuzzy-rule-based classifiers, iterative learning. ...
This paper proposes a novel Adaboost algorithm to learn fuzzy-rule-based classifiers. ...
Adaboost Compared to Genetic Iterative Learning Fuzzy classifier induction with genetic iterative learning [5] operates in three stages. ...
doi:10.1109/tfuzz.2004.825972
fatcat:zgmi62zhdrftpetxxifjftnlsq
Multistep Fuzzy Classifier Design with Self-tuning Coevolutionary Algorithm
english
2013
Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics
english
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This algorithm solves the problem of genetic algorithm parameters setting automatically. ...
Results of numerical experiments for machine learning problems from UCI repository are presented. ...
It allows refusing the genetic algorithm parameters setting without negative effect for algorithm efficiency. The second main result of our work is multistep fuzzy classifier design investigations. ...
doi:10.5220/0004426501130120
dblp:conf/icinco/SergienkoS13
fatcat:7f6nr5bbpfgznczjph6nkujk34
Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing
2007
IEEE transactions on fuzzy systems
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This paper presents a genetic fuzzy system for the data mining task of subgroup discovery, the subgroup discovery iterative genetic algorithm (SDIGA), which obtains fuzzy rules for subgroup discovery in ...
This kind of fuzzy rule allows us to represent knowledge about patterns of interest in an explanatory and understandable form that can be used by the expert. ...
Hybrid Genetic Algorithm for the Induction of a Fuzzy Rule The hybrid GA extracts a single DNF fuzzy rule in an attempt to optimize the confidence and support. ...
doi:10.1109/tfuzz.2006.890662
fatcat:ftho6r54vvhgtkzhn5vcevicv4
Evolutionary Induction of Descriptive Rules in a Market Problem
[chapter]
2005
Studies in Computational Intelligence
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We study the use of Soft Computing methodologies, specifically Fuzzy Logic and Genetic Algorithms, in the design of the Data Mining algorithms most proper to this problem, descriptive induction algorithms ...
Then we present an evolutionary model for the descriptive induction of fuzzy or crisp rules which describe subgroups. ...
Iterative Model for the Extraction of Descriptive Fuzzy Rules The objective of the model for the extraction of descriptive fuzzy rules is to obtain a set of rules which give information on the majority ...
doi:10.1007/11004011_14
fatcat:cnts5mwjwvdbbly7f52u4xboq4
Selection of relevant features in a fuzzy genetic learning algorithm
2001
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
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Structural learning algorithm on vague environment (SLAVE) is a genetic learning algorithm that uses the iterative approach to learn fuzzy rules. ...
Genetic algorithms offer a powerful search method for a variety of learning tasks, and there are different approaches in which they have been applied to learning processes. ...
ACKNOWLEDGMENT The authors wish to thank NOAA for the provision of satellite pictures (both visible and EIR) for TC cases from 1990 to 1998. ...
doi:10.1109/3477.931534
pmid:18244806
fatcat:47rnx7vstjgg5huephylidvdqq
Data Mining by Symbolic Fuzzy Classifiers and Genetic Programming
[chapter]
2013
Advances in Intelligent Systems and Computing
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Among them, hybrid approaches combining two or more algorithms gain importance as the complexity and dimension of real world data sets grows. ...
In the presented application, genetic programming was deployed to evolve a fuzzy classifier and an example of real world application was presented. ...
Fuzzy sets and fuzzy logic can be used for efficient data classification by fuzzy rules and fuzzy classifiers. ...
doi:10.1007/978-3-642-33227-2_28
fatcat:6ef3rneuczayla6f27z5c5tova
Evolutionary Neuro-Fuzzy Systems and Applications
[chapter]
2007
Studies in Computational Intelligence
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The chapter also describes a particular approach that jointly uses neural learning and genetic optimisation to learn a fuzzy model from the given data and to optimise it for accuracy and interpretability ...
For example, the use of a neural fuzzy system and an evolutionary fuzzy system hybridises the approximate reasoning mechanism of fuzzy systems with the learning capabilities of neural networks and evolutionary ...
Structure and Parameter Learning Genetic algorithms may also be applied for structure optimisation of a fuzzy rule base. ...
doi:10.1007/978-3-540-72377-6_2
fatcat:mesecd4qrfasjkxbalcpmpsnoe
Learning cooperative linguistic fuzzy rules using the best-worst ant system algorithm
2005
International Journal of Intelligent Systems
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In this article, we introduce a novel approach to the fuzzy rule learning problem with ant colony optimization (ACO) algorithms. ...
Within the field of linguistic fuzzy modeling with fuzzy rule-based systems, the automatic derivation of the linguistic fuzzy rules from numerical data is an important task. ...
fuzzy model performance. • The third one, proposed by Thrift, 32 is a classical fuzzy rule set learning method based on genetic algorithms. • Moreover, two COR-based methods based on a simulated annealing ...
doi:10.1002/int.20074
fatcat:ehp5iwij7bc4vngu4fhxks6vdm
Hybridization of Fuzzy GBML Approaches for Pattern Classification Problems
2005
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
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We propose a hybrid algorithm of two fuzzy genetics-based machine learning approaches (i.e., Michigan and Pittsburgh) for designing fuzzy rule-based classification systems. ...
Genetic operations for generating new fuzzy rules in the Michigan approach are utilized as a kind of heuristic mutation for partially modifying each rule set. ...
Some fuzzy genetics-based machine learning (GBML) algorithms have been proposed for designing fuzzy rule-based systems without linguistic knowledge from domain experts in the literature (see [3] for ...
doi:10.1109/tsmcb.2004.842257
pmid:15828663
fatcat:qjb34yh3ijdmfpiucohyr6ycdm
A Study on the Analysis of Genetic Algorithms with Various Classification Techniques for Feature Selection
2010
International Journal of Computer Applications
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Genetic algorithms are now days play a vital role among any other methodology in selecting the features based on the Theory of Evolution and on the "Survival of the fitness". ...
Reducing the number of features pave way for various advantaged as well as simplifies the task. ...
The inductive learning of a fuzzy rule-based classification system first of all determines a set of fuzzy rules from the set of instances and patterns. ...
doi:10.5120/1226-1784
fatcat:d6gk37do4vd45ih2ekbf4svemq
Multiobjective Genetic Fuzzy Systems: Review and Future Research Directions
2007
IEEE International Fuzzy Systems conference proceedings
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Recently evolutionary multiobjective algorithms have been used for interpretability-accuracy tradeoff analysis of fuzzy systems. ...
Evolutionary algorithms have been successfully used in many studies to design accurate and interpretable fuzzy systems under the name of genetic fuzzy systems. ...
Thus multiple runs are required to generate a rule set in the iterative rule learning approach. ...
doi:10.1109/fuzzy.2007.4295487
dblp:conf/fuzzIEEE/Ishibuchi07
fatcat:wjbozagl6zevrcauiklvctmd7y
A Hybrid of Self Organized Feature Maps and Parallel Genetic Algorithms for Uncertain Knowledge
2012
International Journal of Computer Applications
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Generating Fuzzy rules using Parallel Genetic Algorithms The process of generating fuzzy rules is responsible for designing the set of fuzzy rules of the knowledge based system. ...
Parallel Genetic Algorithms Parallel genetic algorithms (PGAs) [23] [26] are parallel algorithms. ...
doi:10.5120/9696-4136
fatcat:6tl2ugfsrvahdpuukd2mzkfep4
Host based Anomaly Detection using Fuzzy Genetic Approach (FGA)
2013
International Journal of Computer Applications
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The paper proposes to detect anomalous user behavior on a single machine based on the system log files using fuzzy logic and genetic algorithms. ...
A novel approach for design of fuzzy rules using genetic algorithm is given by S.V. Wong et al. [8] . ...
In this paper, fuzzy logic and genetic algorithms have been used for host based anomaly detection. ...
doi:10.5120/13024-0026
fatcat:gfav4w23grfefh2t4avmuobhdq
Overview of the SLAVE learning algorithm: A review of its evolution and prospects
2014
International Journal of Computational Intelligence Systems
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SLAVE (Structural Learning Algorithm in a Vague Environment) was one of the first fuzzy-rule learning algorithms, and since its first implementation in 1994 it has been frequently used to benchmark new ...
algorithms. ...
In a first stage, we worked on feature construction for genetic learning algorithms based on the iterative learning rule approach. ...
doi:10.1080/18756891.2014.967008
fatcat:gzpe6pszabactnvknzdnsjy4vm
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