Above are papers related to the following paper: [Paper_ID] Paper #119712. 	[Title] 1 FEATURE SUBSET SELECTION USING A GENETIC ALGORITHM time needed for learning a sufficiently accurate [Abstract] Practical pattern classification and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classified. This is due to the fact that the performance of the classifier (usually induced by some learning algorithm) and the cost of classification are sensitive to the choice of the features used to construct the classifier. Exhaustive evaluation of possible feature subsets is usually infeasible in practice because of the large amount of computational effort required. Genetic algorithms, which belong to a class of randomized heuristic search techniques, offer an attractive approach to find near-optimal solutions to such optimization problems. This paper presents an approach to feature subset selection using a genetic algorithm. Some advantages of this approach include the ability to accommodate multiple criteria such as accuracy and cost of classification into the feature selection process and to find feature subsets that perform well for particular choices of the inductive learning algorithm used to construct the pattern classifier. Our experiments with several benchmark real-world pattern classification problems demonstrate the feasibility of this approach to feature subset selection in the automated Many practical pattern classification tasks (e.g., medical diagnosis) require learning of an appropriate classification function that assigns a given input pattern (typically represented using a vector of attribute or feature values) to one of a finite set of classes. The choice of features, attributes, or measurements used to represent patterns that are presented to a classifier affect (among other things): The accuracy of the classification function that can be learned using an inductive learning algorithm (e.g., a decision tree induction algorithm or a neural network learning algorithm): The features used to describe the patterns implicitly define a pattern language. If the language is not expressive enough, it would fail to capture the information that is necessary for classification and hence regardless of the learning algorithm used, the accuracy of the classification function learned would be limited by this lack of information. design of neural networks for pattern classification and knowledge discovery. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #910. 	[Title] Learning networks for face analysis and synthesis [Abstract] This paper presents an overview of the face-related projects in our group. The unifying theme underlying our work is the use of example-based learning methods for both analyzing and synthesizing face images. We label the example face images (and for the problem of face detection, "near miss" faces as well) with descriptive parameters for pose, expression, identity, and face vs. non-face. Then, by using example-based learning techniques, we develop networks for performing analysis tasks such as pose and expression estimation, face recognition, and face detection in cluttered scenes. In addition to these analysis applications, we show how the example-based technique can also be used as a novel method for image synthesis that is for computer graphics. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114118. 	[Title] Power System Security Margin Prediction Using Radial Basis Function Networks [Abstract] Dr. McCalley's research is partially supported through grants from National Science Foundation and Pacific Gas and Electric Company. Dr. Honavar's research is partially supported through grants from National Science Foundation and the John Deere Foundation. This paper will appear in: Proceedings of the 29th Annual North American Power Symposium, Oct. 13-14. 1997, Laramie, Wyoming. Please summarise all the given information.

Paper #119712 has the title `1 FEATURE SUBSET SELECTION USING A GENETIC ALGORITHM time needed for learning a sufficiently accurate
` and the abstract `Practical pattern classification and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classified. This is due to the fact that the performance of the classifier (usually induced by some learning algorithm) and the cost of classification are sensitive to the choice of the features used to construct the classifier. Exhaustive evaluation of possible feature subsets is usually infeasible in practice because of the large amount of computational effort required. Genetic algorithms, which belong to a class of randomized heuristic search techniques, offer an attractive approach to find near-optimal solutions to such optimization problems. This paper presents an approach to feature subset selection using a genetic algorithm. Some advantages of this approach include the ability to accommodate multiple criteria such as accuracy and cost of classification into the feature selection process and to find feature subsets that perform well for particular choices of the inductive learning algorithm used to construct the pattern classifier. Our experiments with several benchmark real-world pattern classification problems demonstrate the feasibility of this approach to feature subset selection in the automated Many practical pattern classification tasks (e.g., medical diagnosis) require learning of an appropriate classification function that assigns a given input pattern (typically represented using a vector of attribute or feature values) to one of a finite set of classes. The choice of features, attributes, or measurements used to represent patterns that are presented to a classifier affect (among other things): The accuracy of the classification function that can be learned using an inductive learning algorithm (e.g., a decision tree induction algorithm or a neural network learning algorithm): The features used to describe the patterns implicitly define a pattern language. If the language is not expressive enough, it would fail to capture the information that is necessary for classification and hence regardless of the learning algorithm used, the accuracy of the classification function learned would be limited by this lack of information. design of neural networks for pattern classification and knowledge discovery.
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Question: Which category should Paper #119712 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #119712 has the title `1 FEATURE SUBSET SELECTION USING A GENETIC ALGORITHM time needed for learning a sufficiently accurate
` and the abstract `Practical pattern classification and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classified. This is due to the fact that the performance of the classifier (usually induced by some learning algorithm) and the cost of classification are sensitive to the choice of the features used to construct the classifier. Exhaustive evaluation of possible feature subsets is usually infeasible in practice because of the large amount of computational effort required. Genetic algorithms, which belong to a class of randomized heuristic search techniques, offer an attractive approach to find near-optimal solutions to such optimization problems. This paper presents an approach to feature subset selection using a genetic algorithm. Some advantages of this approach include the ability to accommodate multiple criteria such as accuracy and cost of classification into the feature selection process and to find feature subsets that perform well for particular choices of the inductive learning algorithm used to construct the pattern classifier. Our experiments with several benchmark real-world pattern classification problems demonstrate the feasibility of this approach to feature subset selection in the automated Many practical pattern classification tasks (e.g., medical diagnosis) require learning of an appropriate classification function that assigns a given input pattern (typically represented using a vector of attribute or feature values) to one of a finite set of classes. The choice of features, attributes, or measurements used to represent patterns that are presented to a classifier affect (among other things): The accuracy of the classification function that can be learned using an inductive learning algorithm (e.g., a decision tree induction algorithm or a neural network learning algorithm): The features used to describe the patterns implicitly define a pattern language. If the language is not expressive enough, it would fail to capture the information that is necessary for classification and hence regardless of the learning algorithm used, the accuracy of the classification function learned would be limited by this lack of information. design of neural networks for pattern classification and knowledge discovery.
`.
Question: Which category should Paper #119712 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Genetic_Algorithms