PKNN-MIFS: A Parallel KNN Classifier over an Optimal Subset of Features
Feature selection help select an optimal subset of features from a large feature space to achieve better classification performance. The performance of KNN classifier can be improved significantly using an appropriate subset of features from a large feature space. Recent development in General Purpo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-05-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266730532200014X |
| _version_ | 1811226429029875712 |
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| author | Shashank Shekhar Nazrul Hoque Dhruba K. Bhattacharyya |
| author_facet | Shashank Shekhar Nazrul Hoque Dhruba K. Bhattacharyya |
| author_sort | Shashank Shekhar |
| collection | DOAJ |
| description | Feature selection help select an optimal subset of features from a large feature space to achieve better classification performance. The performance of KNN classifier can be improved significantly using an appropriate subset of features from a large feature space. Recent development in General Purpose Graphics Processing Units (GPGPU) has provided us a low cost yet high performance computing support for wide range of applications. This paper presents a parallel KNN classifier powered by a mutual information based feature selection called PKNN-MIFS for effective classification of real life data. It selects an optimal subset of features from the original feature set by exploiting the mutual information concept for the estimation of feature-class and feature-feature relevance. It selects a non-redundant feature by giving higher priority on feature-class relevance. The performance of the proposed PKNN-MIFS has been evaluated over several datasets and has been found to be superior to its closed counterpart. |
| first_indexed | 2024-04-12T09:24:58Z |
| format | Article |
| id | doaj.art-b3ed1c42862841dabf059f526bb74e35 |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-04-12T09:24:58Z |
| publishDate | 2022-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-b3ed1c42862841dabf059f526bb74e352022-12-22T03:38:30ZengElsevierIntelligent Systems with Applications2667-30532022-05-0114200073PKNN-MIFS: A Parallel KNN Classifier over an Optimal Subset of FeaturesShashank Shekhar0Nazrul Hoque1Dhruba K. Bhattacharyya2Department of Computer Science & Engineering, Tezpur University Napaam, Tezpur 784028, Assam, IndiaCorresponding author.; Department of Computer Science, Manipur University, Canchipur, Imphal, Manipur 795003, IndiaDepartment of Computer Science & Engineering, Tezpur University Napaam, Tezpur 784028, Assam, IndiaFeature selection help select an optimal subset of features from a large feature space to achieve better classification performance. The performance of KNN classifier can be improved significantly using an appropriate subset of features from a large feature space. Recent development in General Purpose Graphics Processing Units (GPGPU) has provided us a low cost yet high performance computing support for wide range of applications. This paper presents a parallel KNN classifier powered by a mutual information based feature selection called PKNN-MIFS for effective classification of real life data. It selects an optimal subset of features from the original feature set by exploiting the mutual information concept for the estimation of feature-class and feature-feature relevance. It selects a non-redundant feature by giving higher priority on feature-class relevance. The performance of the proposed PKNN-MIFS has been evaluated over several datasets and has been found to be superior to its closed counterpart.http://www.sciencedirect.com/science/article/pii/S266730532200014XKNN ClassifierFeature selectionPyCUDARelevanceGPU |
| spellingShingle | Shashank Shekhar Nazrul Hoque Dhruba K. Bhattacharyya PKNN-MIFS: A Parallel KNN Classifier over an Optimal Subset of Features Intelligent Systems with Applications KNN Classifier Feature selection PyCUDA Relevance GPU |
| title | PKNN-MIFS: A Parallel KNN Classifier over an Optimal Subset of Features |
| title_full | PKNN-MIFS: A Parallel KNN Classifier over an Optimal Subset of Features |
| title_fullStr | PKNN-MIFS: A Parallel KNN Classifier over an Optimal Subset of Features |
| title_full_unstemmed | PKNN-MIFS: A Parallel KNN Classifier over an Optimal Subset of Features |
| title_short | PKNN-MIFS: A Parallel KNN Classifier over an Optimal Subset of Features |
| title_sort | pknn mifs a parallel knn classifier over an optimal subset of features |
| topic | KNN Classifier Feature selection PyCUDA Relevance GPU |
| url | http://www.sciencedirect.com/science/article/pii/S266730532200014X |
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