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Publication DatePattern Recognition, ISSN 0031-3203, 2010, Volume 43, Issue 1, pp. 5 - 13
Searching for an optimal feature subset from a high dimensional feature space is known to be an NP-complete problem. We present a hybrid algorithm, SAGA, for...
Dimensionality reduction | Feature subset selection | Curse of dimensionality | High dimensionality | SUPPORT VECTOR MACHINES | CANCER CLASSIFICATION | SEARCH | RELEVANCE | ANT COLONY OPTIMIZATION | ALGORITHM | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Neural networks | Mathematical optimization | Algorithms
Dimensionality reduction | Feature subset selection | Curse of dimensionality | High dimensionality | SUPPORT VECTOR MACHINES | CANCER CLASSIFICATION | SEARCH | RELEVANCE | ANT COLONY OPTIMIZATION | ALGORITHM | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Neural networks | Mathematical optimization | Algorithms
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Loading RightsInformation Sciences, ISSN 0020-0255, 06/2019, Volume 486, pp. 393 - 418
The Column Subset Selection Problem is a hard combinatorial optimization problem that provides a natural framework for unsupervised feature selection, and...
Feature selection | Unsupervised learning | Column subset selection | REGRESSION | MATRIX | UNSUPERVISED FEATURE-SELECTION | COMPUTER SCIENCE, INFORMATION SYSTEMS | ALGORITHMS | Algorithms
Feature selection | Unsupervised learning | Column subset selection | REGRESSION | MATRIX | UNSUPERVISED FEATURE-SELECTION | COMPUTER SCIENCE, INFORMATION SYSTEMS | ALGORITHMS | Algorithms
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Loading RightsKnowledge and Information Systems, ISSN 0219-1377, 1/2018, Volume 54, Issue 1, pp. 65 - 94
Dimensionality reduction is often a crucial step for the successful application of machine learning and data mining methods. One way to achieve said reduction...
Information Systems Applications (incl.Internet) | Dimensionality reduction | Unsupervised feature selection | Column subset selection | Computer Science | Database Management | Data Mining and Knowledge Discovery | Information Storage and Retrieval | Machine learning | Information Systems and Communication Service | IT in Business | Data mining | UNSUPERVISED FEATURE-SELECTION | RELEVANCE | FACE RECOGNITION | DECOMPOSITION | COMPUTER SCIENCE, INFORMATION SYSTEMS | RANK | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Algorithms | Analysis | Reduction | Iterative algorithms | Iterative methods | Information systems
Information Systems Applications (incl.Internet) | Dimensionality reduction | Unsupervised feature selection | Column subset selection | Computer Science | Database Management | Data Mining and Knowledge Discovery | Information Storage and Retrieval | Machine learning | Information Systems and Communication Service | IT in Business | Data mining | UNSUPERVISED FEATURE-SELECTION | RELEVANCE | FACE RECOGNITION | DECOMPOSITION | COMPUTER SCIENCE, INFORMATION SYSTEMS | RANK | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Algorithms | Analysis | Reduction | Iterative algorithms | Iterative methods | Information systems
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Loading RightsInformation Sciences, ISSN 0020-0255, 2008, Volume 178, Issue 18, pp. 3577 - 3594
Feature subset selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Most of researches are focused...
Categorical feature | Feature selection | Heterogeneous feature | Numerical feature | Rough sets | Neighborhood | rough sets | APPROXIMATION | ALGORITHM | COMPUTER SCIENCE, INFORMATION SYSTEMS | CLASSIFICATION | GRANULATION | heterogeneous feature | CLASSIFIERS | REDUCTION | categorical feature | FUZZY-LOGIC | numerical feature | SYSTEMS | neighborhood | feature selection
Categorical feature | Feature selection | Heterogeneous feature | Numerical feature | Rough sets | Neighborhood | rough sets | APPROXIMATION | ALGORITHM | COMPUTER SCIENCE, INFORMATION SYSTEMS | CLASSIFICATION | GRANULATION | heterogeneous feature | CLASSIFIERS | REDUCTION | categorical feature | FUZZY-LOGIC | numerical feature | SYSTEMS | neighborhood | feature selection
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Loading RightsIEEE Transactions on Knowledge and Data Engineering, ISSN 1041-4347, 01/2013, Volume 25, Issue 1, pp. 1 - 14
Feature selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature...
Feature subset selection | Correlation | Clustering algorithms | graph-based clustering | Markov processes | Prediction algorithms | Feature extraction | feature clustering | Complexity theory | Partitioning algorithms | filter method | INFORMATION | RELEVANCE | COMPUTER SCIENCE, INFORMATION SYSTEMS | STATISTICAL COMPARISONS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | CLASSIFIERS | ENGINEERING, ELECTRICAL & ELECTRONIC | Cluster analysis | Usage | Electronic data processing | Innovations | Graph theory | Distribution (Probability theory) | Research | Methods
Feature subset selection | Correlation | Clustering algorithms | graph-based clustering | Markov processes | Prediction algorithms | Feature extraction | feature clustering | Complexity theory | Partitioning algorithms | filter method | INFORMATION | RELEVANCE | COMPUTER SCIENCE, INFORMATION SYSTEMS | STATISTICAL COMPARISONS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | CLASSIFIERS | ENGINEERING, ELECTRICAL & ELECTRONIC | Cluster analysis | Usage | Electronic data processing | Innovations | Graph theory | Distribution (Probability theory) | Research | Methods
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Loading RightsArtificial Intelligence, ISSN 0004-3702, 1997, Volume 97, Issue 1, pp. 273 - 324
In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its...
Wrapper | Feature selection | Filter | Classification | filter | LEARNING ALGORITHMS | IRRELEVANT | wrapper | classification | feature selection | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Wrapper | Feature selection | Filter | Classification | filter | LEARNING ALGORITHMS | IRRELEVANT | wrapper | classification | feature selection | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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Loading RightsKnowledge-Based Systems, ISSN 0950-7051, 11/2016, Volume 111, pp. 173 - 179
Rough set theory has been extensively discussed in machine learning and pattern recognition. It provides us another important theoretical tool for feature...
Fuzzy neighborhood | Feature selection | Fuzzy decision | Rough set model | ATTRIBUTE REDUCTION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Analysis | Algorithms | Machine learning
Fuzzy neighborhood | Feature selection | Fuzzy decision | Rough set model | ATTRIBUTE REDUCTION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Analysis | Algorithms | Machine learning
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Loading RightsNeurocomputing, ISSN 0925-2312, 01/2015, Volume 147, Issue 1, pp. 271 - 279
Feature selection is an important task for data analysis and information retrieval processing, pattern classification systems, and data mining applications. It...
Wrapper | Binary ACO | Feature selection | Ant colony optimization (ACO) | Classification | PARTICLE SWARM OPTIMIZATION | NEURAL-NETWORKS | HYBRID GENETIC ALGORITHM | ANT COLONY OPTIMIZATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Information storage and retrieval | Electrical engineering | Algorithms | Data mining | Mathematical optimization | Analysis
Wrapper | Binary ACO | Feature selection | Ant colony optimization (ACO) | Classification | PARTICLE SWARM OPTIMIZATION | NEURAL-NETWORKS | HYBRID GENETIC ALGORITHM | ANT COLONY OPTIMIZATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Information storage and retrieval | Electrical engineering | Algorithms | Data mining | Mathematical optimization | Analysis
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Loading RightsPattern Recognition, ISSN 0031-3203, 11/2004, Volume 37, Issue 11, pp. 2165 - 2176
Past work on object detection has emphasized the issues of feature extraction and classification, however, relatively less attention has been given to the...
Support vector machines | Feature subset selection | Vehicle detection | Face detection | Genetic algorithms | vehicle detection | NEURAL NETWORKS | feature subset selection | support vector machines | RECOGNITION | face detection | IMAGES | genetic algorithms | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Case studies | Computer science | Algorithms | Machine vision
Support vector machines | Feature subset selection | Vehicle detection | Face detection | Genetic algorithms | vehicle detection | NEURAL NETWORKS | feature subset selection | support vector machines | RECOGNITION | face detection | IMAGES | genetic algorithms | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Case studies | Computer science | Algorithms | Machine vision
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Loading RightsInformation Sciences, ISSN 0020-0255, 10/2014, Volume 281, pp. 128 - 146
A new method for feature subset selection in machine learning, FSS-MGSA (Feature Subset Selection by Modified Gravitational Search Algorithm), is presented....
Feature subset selection | Gravitational search algorithm | Chaotic map | Sequential quadratic programming | Learning algorithm | Classification | SYSTEM | SUPPORT VECTOR MACHINES | TABU SEARCH | COMPUTER SCIENCE, INFORMATION SYSTEMS | EXPRESSION DATA | GENETIC ALGORITHMS | PARTICLE SWARM OPTIMIZATION | IMAGE RETRIEVAL | EVOLUTIONARY INSTANCE | REDUCTION | CLASSIFICATION LEARNING ALGORITHMS | Algorithms | Machine learning | Search algorithms | Chaos theory | Evolutionary | MAP (programming language) | Optimization | Biological diversity | Genetic algorithms
Feature subset selection | Gravitational search algorithm | Chaotic map | Sequential quadratic programming | Learning algorithm | Classification | SYSTEM | SUPPORT VECTOR MACHINES | TABU SEARCH | COMPUTER SCIENCE, INFORMATION SYSTEMS | EXPRESSION DATA | GENETIC ALGORITHMS | PARTICLE SWARM OPTIMIZATION | IMAGE RETRIEVAL | EVOLUTIONARY INSTANCE | REDUCTION | CLASSIFICATION LEARNING ALGORITHMS | Algorithms | Machine learning | Search algorithms | Chaos theory | Evolutionary | MAP (programming language) | Optimization | Biological diversity | Genetic algorithms
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Loading RightsApplied Soft Computing, ISSN 1568-4946, 06/2016, Volume 43, pp. 117 - 130
The proposed method uses a local search technique which is embedded in particle swarm optimization (PSO) to select the reduced sized and salient feature...
Local search | Feature selection | Particle swarm optimization | Correlation information | SUPPORT VECTOR MACHINES | UNSUPERVISED FEATURE-SELECTION | ANT COLONY OPTIMIZATION | CLASSIFICATION | REDUNDANCY | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | GENETIC ALGORITHM | ROUGH SETS | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MUTUAL INFORMATION | NEURAL-NETWORKS | PARAMETER DETERMINATION | Mathematical optimization
Local search | Feature selection | Particle swarm optimization | Correlation information | SUPPORT VECTOR MACHINES | UNSUPERVISED FEATURE-SELECTION | ANT COLONY OPTIMIZATION | CLASSIFICATION | REDUNDANCY | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | GENETIC ALGORITHM | ROUGH SETS | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MUTUAL INFORMATION | NEURAL-NETWORKS | PARAMETER DETERMINATION | Mathematical optimization
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Loading RightsExpert Systems With Applications, ISSN 0957-4174, 12/2019, Volume 137, pp. 11 - 21
Performance of evolutionary algorithms depends on many factors such as population size, number of generations, crossover or mutation probability, etc....
Feature subset selection | Initial population | Multiobjective optimization | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | GENETIC ALGORITHM | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms | Machine learning | Statistical analysis | Search algorithms | Redundancy | Artificial neural networks | Evolutionary algorithms | Optimization | Genetic algorithms | Datasets | Support vector machines | Teaching methods | Data sets | Population | Mutation | Expert systems | Methods
Feature subset selection | Initial population | Multiobjective optimization | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | GENETIC ALGORITHM | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms | Machine learning | Statistical analysis | Search algorithms | Redundancy | Artificial neural networks | Evolutionary algorithms | Optimization | Genetic algorithms | Datasets | Support vector machines | Teaching methods | Data sets | Population | Mutation | Expert systems | Methods
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Loading RightsSIAM Journal on Matrix Analysis and Applications, ISSN 0895-4798, 2013, Volume 34, Issue 4, pp. 1464 - 1499
We study the following problem of subset selection for matrices: given a matrix X is an element of R-nxm (m > n) and a sampling parameter k (n <= k <= m),...
Feature selection | Volume sampling | Sparse approximation | Low-rank approximations | Low-stretch spanning trees | Subset selection | K-means clustering | low-stretch spanning trees | volume sampling | subset selection | MATHEMATICS, APPLIED | k-means clustering | low-rank approximations | APPROXIMATION | sparse approximation | RANK | ALGORITHMS | feature selection
Feature selection | Volume sampling | Sparse approximation | Low-rank approximations | Low-stretch spanning trees | Subset selection | K-means clustering | low-stretch spanning trees | volume sampling | subset selection | MATHEMATICS, APPLIED | k-means clustering | low-rank approximations | APPROXIMATION | sparse approximation | RANK | ALGORITHMS | feature selection
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Loading RightsImage and Vision Computing, ISSN 0262-8856, 08/2013, Volume 31, Issue 8, pp. 580 - 591
In this paper, we tackle the problem of gait recognition based on the model-free approach. Numerous methods exist; they all lead to high dimensional feature...
Feature selection | Random forest | Model-free | Panoramic | Gait recognition | COMPUTER SCIENCE, SOFTWARE ENGINEERING | IMAGE | SYMMETRY | COMPUTER SCIENCE, THEORY & METHODS | OPTICS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Forests | Algorithms | Searching | Space probes | Classification | Strategy | Masks
Feature selection | Random forest | Model-free | Panoramic | Gait recognition | COMPUTER SCIENCE, SOFTWARE ENGINEERING | IMAGE | SYMMETRY | COMPUTER SCIENCE, THEORY & METHODS | OPTICS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Forests | Algorithms | Searching | Space probes | Classification | Strategy | Masks
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Loading RightsNeurocomputing, ISSN 0925-2312, 11/2015, Volume 168, pp. 706 - 718
Conventional mutual information (MI) based feature selection (FS) methods are unable to handle heterogeneous feature subset selection properly because of data...
Feature subset selection | Feature transformation | Heterogeneous features | Mutual information | DIMENSIONALITY REDUCTION | DISCRETIZATION | SEARCH | ALGORITHM | HISTOGRAM | CLASSIFICATION | SIMILARITY |
Feature subset selection | Feature transformation | Heterogeneous features | Mutual information | DIMENSIONALITY REDUCTION | DISCRETIZATION | SEARCH | ALGORITHM | HISTOGRAM | CLASSIFICATION | SIMILARITY |