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135
Dimensionality Reduction Using Genetic Algorithms
, 2000
"... Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern has a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving ..."
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Cited by 140 (11 self)
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Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern has a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to reduce the cost of feature measurement, increase classifier efficiency, and allow higher classification accuracy. Many current feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and increasing classification efficiency, it does not necessarily reduce the number of features that must be measured, since each new feature may be a linear combination of all of the features in the original pattern vector. Here we present a new approach to feature extraction in which feature selection, feature extraction, and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a vector of feature weights, which are used to scale the individual features in the original pattern vectors in either a linear or a nonlinear fashion. A masking vector is also employed to perform simultaneous selection of a subset of the features. We employ this technique in combination with the knearestneighbor classification rule, and compare the results with classical feature selection and extraction techniques, including sequential floating forward feature selection, and linear discriminant analysis. We also present results for identification of favorable water binding sites on protein surfaces, an important problem in biochemistry and drug design.
Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems
 IEEE TRANS. SYSTEMS, MAN CYBERNETICS—PART B: CYBERNET
, 1999
"... We examine the performance of a fuzzy geneticsbased machine learning method for multidimensional pattern classification problems with continuous attributes. In our method, each fuzzy if–then rule is handled as an individual, and a fitness value is assigned to each rule. Thus, our method can be vi ..."
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Cited by 95 (11 self)
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We examine the performance of a fuzzy geneticsbased machine learning method for multidimensional pattern classification problems with continuous attributes. In our method, each fuzzy if–then rule is handled as an individual, and a fitness value is assigned to each rule. Thus, our method can be viewed as a classifier system. In this paper, we first describe fuzzy if–then rules and fuzzy reasoning for pattern classification problems. Then we explain a geneticsbased machine learning method that automatically generates fuzzy if–then rules for pattern classification problems from numerical data. Because our method uses linguistic values with fixed membership functions as antecedent fuzzy sets, a linguistic interpretation of each fuzzy if–then rule is easily obtained. The fixed membership functions also lead to a simple implementation of our method as a computer program. The simplicity of implementation and the linguistic interpretation of the generated fuzzy if–then rules are the main characteristic features of our method. The performance of our method is evaluated by computer simulations on some wellknown test problems. While our method involves no tuning mechanism of membership functions, it works very well in comparison with other classification methods such as nonfuzzy machine learning techniques and neural networks.
An online selfconstructing neural fuzzy inference network and its applications
 IEEE. TRANS. FUZZY. SYS
, 1998
"... A selfconstructing neural fuzzy inference network (SONFIN) with online learning ability is proposed in this paper. The SONFIN is inherently a modified Takagi–Sugeno–Kang (TSK)type fuzzy rulebased model possessing neural network’s learning ability. There are no rules initially in the SONFIN. The ..."
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Cited by 92 (22 self)
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A selfconstructing neural fuzzy inference network (SONFIN) with online learning ability is proposed in this paper. The SONFIN is inherently a modified Takagi–Sugeno–Kang (TSK)type fuzzy rulebased model possessing neural network’s learning ability. There are no rules initially in the SONFIN. They are created and adapted as online learning proceeds via simultaneous structure and parameter identification. In the structure identification of the precondition part, the input space is partitioned in a flexible way according to a aligned clusteringbased algorithm. As to the structure identification of the consequent part, only a singleton value selected by a clustering method is assigned to each rule initially. Afterwards, some additional significant terms (input variables) selected via a projectionbased correlation measure for each rule will be added to the consequent part (forming a linear equation of input variables) incrementally as learning proceeds. The combined precondition and consequent structure identification scheme can set up an economic and dynamically growing network, a main feature of the SONFIN. In the parameter identification, the consequent parameters are tuned optimally by either least mean squares (LMS) or recursive least squares (RLS) algorithms and the precondition parameters are tuned by backpropagation algorithm. Both the structure and parameter identification are done simultaneously to form a fast learning scheme, which is another feature of the SONFIN. Furthermore, to enhance the knowledge representation ability of the SONFIN, a linear transformation for each input variable can be incorporated into the network so that much fewer rules are needed or higher accuracy can be achieved. Proper linear transformations are also learned dynamically in the parameter identification phase of the SONFIN. To demonstrate the capability of the proposed SONFIN, simulations in different areas including control, communication, and signal processing are done. Effectiveness of the SONFIN is verified from these simulations.
An Indexed Bibliography of Genetic Algorithms in Power Engineering
, 1995
"... s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceed ..."
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Cited by 90 (10 self)
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s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986  Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987  1992 ffl EI M: The Engineering Index Monthly: Jan. 1993  Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina GorgesSchleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...
Designing fuzzy inference systems from data: an interpretabilityoriented review
 IEEE TRANS. FUZZY SYSTEMS
, 2001
"... Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from ..."
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Cited by 88 (16 self)
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Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation and system optimization. Rule generation leads to a basic system with a given space partitioning and the corresponding set of rules. System optimization can be done at various levels. Variable selection can be an overall selection or it can be managed rule by rule. Rule base optimization aims to select the most useful rules and to optimize rule conclusions. Space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters. Structure optimization is of a major importance: selecting variables, reducing the rule base and optimizing the number of fuzzy sets. Over the years, many methods have become available for designing FIS from data. Their efficiency is usually characterized by a numerical performance index. However, for humancomputer cooperation another criterion is needed: the rule interpretability. An implicit assumption states that fuzzy rules are by nature easy to be interpreted. This could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts. This paper analyzes the main methods for automatic rule generation and structure optimization. They are grouped into several families and compared according to the rule interpretability criterion. For this purpose, three conditions for a set of rules to be interpretable are defined.
Fuzzy rule selection by multiobjective genetic local search algorithms and rule evaluation measures in data mining
, 2004
"... ..."
Implementation of evolutionary fuzzy systems
 IEEE Transactions on Fuzzy Systems
, 1999
"... Abstract — In this paper, evolutionary fuzzy systems are discussed in which the membership function shapes and types and the fuzzy rule set including the number of rules inside it are evolved using a genetic (evolutionary) algorithm. In addition, the genetic parameters (operators) of the evolutionar ..."
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Cited by 51 (2 self)
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Abstract — In this paper, evolutionary fuzzy systems are discussed in which the membership function shapes and types and the fuzzy rule set including the number of rules inside it are evolved using a genetic (evolutionary) algorithm. In addition, the genetic parameters (operators) of the evolutionary algorithm are adapted via a fuzzy system. Benefits of the methodology are illustrated in the process of classifying the iris data set. Possible extensions of the methods are summarized. Index Terms—Fuzzy expert systems, genetic algorithm, membership.
Genetic Tuning of Fuzzy Rule Deep Structures for Linguistic Modeling
 IEEE TRANS. ON FUZZY SYSTEMS
, 2001
"... Tuning fuzzy rulebased systems for Linguistic Modeling is an interesting and widely developed task. It involves adjusting some of the components composing the knowledge base without completely redefining it. To do that, as the fuzzy rule symbolic representations (known as fuzzy rule surface structu ..."
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Cited by 50 (13 self)
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Tuning fuzzy rulebased systems for Linguistic Modeling is an interesting and widely developed task. It involves adjusting some of the components composing the knowledge base without completely redefining it. To do that, as the fuzzy rule symbolic representations (known as fuzzy rule surface structures) as the meaning of the involved membership functions (which together with the surface structures are known as fuzzy rule deep structures) may be modified. This contribution introduces a genetic tuning process for jointly fitting these two components, i.e., whole deep structures. To adjust the symbolic representations, we propose to use linguistic hedges to perform slight modifications keeping a good interpretability. To change the membership function meanings, two different ways considering basic or extended expressions are proposed. As the accomplished experimental study shows, the good performance of our proposal mainly lies in the consideration of this tuning approach performed at two different levels of significance.
Improving the interpretability of TSK fuzzy models by combining global and local learning
 IEEE TRANS. FUZZY SYST
, 1998
"... The fuzzy inference system proposed by Takagi, Sugeno, and Kang, known as the TSK model in fuzzy system literature, provides a powerful tool for modeling complex nonlinear systems. Unlike conventional modeling where a single model is used to describe the global behavior of a system, TSK modeling is ..."
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Cited by 47 (1 self)
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The fuzzy inference system proposed by Takagi, Sugeno, and Kang, known as the TSK model in fuzzy system literature, provides a powerful tool for modeling complex nonlinear systems. Unlike conventional modeling where a single model is used to describe the global behavior of a system, TSK modeling is essentially a multimodel approach in which simple submodels (typically linear models) are combined to describe the global behavior of the system. Most existing learning algorithms for identifying the TSK model are based on minimizing the square of the residual between the overall outputs of the real system and the identified model. Although these algorithms can generate a TSK model with good global performance (i.e., the model is capable of approximating the given system with arbitrary accuracy, provided that sufficient rules are used and sufficient training data are available), they cannot guarantee the resulting model to have a good local performance. Often, the submodels in the TSK model may exhibit an erratic local behavior, which is difficult to interpret. Since one of the important motivations of using the TSK model (also other fuzzy models) is to gain insights into the model, it is important to investigate the interpretability issue of the TSK model. In this paper, we propose a new learning algorithm that integrates global learning and local learning in a single algorithmic framework. This algorithm uses the idea of local weighed regression and local approximation in nonparametric statistics, but remains the component of global fitting in the existing learning algorithms. The algorithm is capable of adjusting its parameters based on the user’s preference, generating models with good tradeoff in terms of global fitting and local interpretation. We illustrate the performance of the proposed algorithm using a motorcycle crash modeling example.
Generating the knowledge base of a fuzzy rulebased system by the genetic learning of the data base
 IEEE Tran. Fuzzy Systems
"... Abstract—A new method is proposed to automatically learn the knowledge base (KB) by finding an appropiate data base (DB) by means of a genetic algorithm while using a simple generation method to derive the rule base (RB). Our genetic process learns the number of linguistic terms per variable and the ..."
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Cited by 46 (14 self)
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Abstract—A new method is proposed to automatically learn the knowledge base (KB) by finding an appropiate data base (DB) by means of a genetic algorithm while using a simple generation method to derive the rule base (RB). Our genetic process learns the number of linguistic terms per variable and the membership function parameters that define their semantics, while a rule base generation method learns the number of rules and their composition. Index Terms—Fuzzy rulebased systems, data base, learning, genetic algorithms. I.