Inhaltsangabe
Feature selection is very important task in classification. Number of features is available for classification but not all of them are useful. Irrelevant and redundant features may even reduce the performance. There are two types of feature selection approaches. They are wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm when searching the goodness of the features during the feature selection process while filters are independent of any classification algorithm. The goal of Feature selection is to choose a small number of relevant features to achieve similar or even better classification performance than using all features. Existing feature selection algorithms treat the task as a single objective problem. The proposed system can treat as a multi objective problem. It has two objectives. They are maximizing the classification performance and minimizing the number of features. The proposed system is PSO-based multi-objective feature selection algorithm. The algorithm (NSPSOFS) introduces the task is to generate a Pareto front of non dominated solutions idea of non dominated sorting into PSO to address feature selection problems.
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Über die Autorin bzw. den Autor
Le Dr Y Mohana Roopa travaille actuellement comme professeur de CSE, IARE Hyderabad. Elle a 18 ans d'expérience dans l'enseignement et la recherche et a guidé plus de 15 étudiants MTech. Elle a publié 15 articles dans diverses conférences nationales et internationales et 25 articles dans diverses revues internationales. Elle a publié deux livres.
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A Multi-Objective Particle Swarm Optimization For Feature Selection
Verlag:
LAP LAMBERT Academic Publishing Jul 2018, 2018
ISBN 10: 6139864631
ISBN 13: 9786139864638
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Feature selection is very important task in classification. Number of features is available for classification but not all of them are useful. Irrelevant and redundant features may even reduce the performance. There are two types of feature selection approaches. They are wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm when searching the goodness of the features during the feature selection process while filters are independent of any classification algorithm. The goal of Feature selection is to choose a small number of relevant features to achieve similar or even better classification performance than using all features. Existing feature selection algorithms treat the task as a single objective problem. The proposed system can treat as a multi objective problem. It has two objectives. They are maximizing the classification performance and minimizing the number of features. The proposed system is PSO-based multi-objective feature selection algorithm. The algorithm (NSPSOFS) introduces the task is to generate a Pareto front of non dominated solutions idea of non dominated sorting into PSO to address feature selection problems. 60 pp. Englisch. Bestandsnummer des Verkäufers 9786139864638
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A Multi-Objective Particle Swarm Optimization For Feature Selection
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ISBN 13: 9786139864638
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Roopa Y. MohanaDr. Y Mohana Roopa is the Professor of Computer science and Engineering. She is also Dean of Continuing Education and Internal Quality Assurance at Institute of Aeronautical Engineering. She has 18 years of experience . Bestandsnummer des Verkäufers 385874877
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A Multi-Objective Particle Swarm Optimization For Feature Selection
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ISBN 10: 6139864631
ISBN 13: 9786139864638
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A Multi-Objective Particle Swarm Optimization For Feature Selection
Verlag:
LAP LAMBERT Academic Publishing Jul 2018, 2018
ISBN 10: 6139864631
ISBN 13: 9786139864638
Neu
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Feature selection is very important task in classification. Number of features is available for classification but not all of them are useful. Irrelevant and redundant features may even reduce the performance. There are two types of feature selection approaches. They are wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm when searching the goodness of the features during the feature selection process while filters are independent of any classification algorithm. The goal of Feature selection is to choose a small number of relevant features to achieve similar or even better classification performance than using all features. Existing feature selection algorithms treat the task as a single objective problem. The proposed system can treat as a multi objective problem. It has two objectives. They are maximizing the classification performance and minimizing the number of features. The proposed system is PSO-based multi-objective feature selection algorithm. The algorithm (NSPSOFS) introduces the task is to generate a Pareto front of non dominated solutions idea of non dominated sorting into PSO to address feature selection problems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch. Bestandsnummer des Verkäufers 9786139864638
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A Multi-Objective Particle Swarm Optimization For Feature Selection
Verlag:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139864631
ISBN 13: 9786139864638
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Feature selection is very important task in classification. Number of features is available for classification but not all of them are useful. Irrelevant and redundant features may even reduce the performance. There are two types of feature selection approaches. They are wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm when searching the goodness of the features during the feature selection process while filters are independent of any classification algorithm. The goal of Feature selection is to choose a small number of relevant features to achieve similar or even better classification performance than using all features. Existing feature selection algorithms treat the task as a single objective problem. The proposed system can treat as a multi objective problem. It has two objectives. They are maximizing the classification performance and minimizing the number of features. The proposed system is PSO-based multi-objective feature selection algorithm. The algorithm (NSPSOFS) introduces the task is to generate a Pareto front of non dominated solutions idea of non dominated sorting into PSO to address feature selection problems. Bestandsnummer des Verkäufers 9786139864638
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A Multi-Objective Particle Swarm Optimization For Feature Selection
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LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139864631
ISBN 13: 9786139864638
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Taschenbuch. Zustand: Neu. A Multi-Objective Particle Swarm Optimization For Feature Selection | Y. Mohana Roopa (u. a.) | Taschenbuch | 60 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139864638 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 114188327
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