Analysis of Dimensionality Reduction Techniques on Big Data
Due to digitization, a huge volume of data is being generated across several sectors such as healthcare, production, sales, IoT devices, Web, organizations. Machine learning algorithms are used to uncover patterns among the attributes of this data. Hence, they can be used to make predictions that ca...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9036908/ |
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| author | G. Thippa Reddy M. Praveen Kumar Reddy Kuruva Lakshmanna Rajesh Kaluri Dharmendra Singh Rajput Gautam Srivastava Thar Baker |
| author_facet | G. Thippa Reddy M. Praveen Kumar Reddy Kuruva Lakshmanna Rajesh Kaluri Dharmendra Singh Rajput Gautam Srivastava Thar Baker |
| author_sort | G. Thippa Reddy |
| collection | DOAJ |
| description | Due to digitization, a huge volume of data is being generated across several sectors such as healthcare, production, sales, IoT devices, Web, organizations. Machine learning algorithms are used to uncover patterns among the attributes of this data. Hence, they can be used to make predictions that can be used by medical practitioners and people at managerial level to make executive decisions. Not all the attributes in the datasets generated are important for training the machine learning algorithms. Some attributes might be irrelevant and some might not affect the outcome of the prediction. Ignoring or removing these irrelevant or less important attributes reduces the burden on machine learning algorithms. In this work two of the prominent dimensionality reduction techniques, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are investigated on four popular Machine Learning (ML) algorithms, Decision Tree Induction, Support Vector Machine (SVM), Naive Bayes Classifier and Random Forest Classifier using publicly available Cardiotocography (CTG) dataset from University of California and Irvine Machine Learning Repository. The experimentation results prove that PCA outperforms LDA in all the measures. Also, the performance of the classifiers, Decision Tree, Random Forest examined is not affected much by using PCA and LDA.To further analyze the performance of PCA and LDA the eperimentation is carried out on Diabetic Retinopathy (DR) and Intrusion Detection System (IDS) datasets. Experimentation results prove that ML algorithms with PCA produce better results when dimensionality of the datasets is high. When dimensionality of datasets is low it is observed that the ML algorithms without dimensionality reduction yields better results. |
| first_indexed | 2024-12-22T19:27:47Z |
| format | Article |
| id | doaj.art-1e5a7e20569547b6b4b0e8c2d57a85fe |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:27:47Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1e5a7e20569547b6b4b0e8c2d57a85fe2022-12-21T18:15:12ZengIEEEIEEE Access2169-35362020-01-018547765478810.1109/ACCESS.2020.29809429036908Analysis of Dimensionality Reduction Techniques on Big DataG. Thippa Reddy0https://orcid.org/0000-0003-0097-801XM. Praveen Kumar Reddy1https://orcid.org/0000-0003-4209-2495Kuruva Lakshmanna2Rajesh Kaluri3https://orcid.org/0000-0003-2073-9833Dharmendra Singh Rajput4Gautam Srivastava5https://orcid.org/0000-0001-9851-4103Thar Baker6https://orcid.org/0000-0002-5166-4873School of Infromation Technology and Engineering, VIT, Vellore, IndiaSchool of Infromation Technology and Engineering, VIT, Vellore, IndiaSchool of Infromation Technology and Engineering, VIT, Vellore, IndiaSchool of Infromation Technology and Engineering, VIT, Vellore, IndiaSchool of Infromation Technology and Engineering, VIT, Vellore, IndiaDepartment of Mathematics and Computer Science, Brandon University, Brandon, CanadaDepartment of Computer Science, Liverpool John Moores University, Liverpool, U.KDue to digitization, a huge volume of data is being generated across several sectors such as healthcare, production, sales, IoT devices, Web, organizations. Machine learning algorithms are used to uncover patterns among the attributes of this data. Hence, they can be used to make predictions that can be used by medical practitioners and people at managerial level to make executive decisions. Not all the attributes in the datasets generated are important for training the machine learning algorithms. Some attributes might be irrelevant and some might not affect the outcome of the prediction. Ignoring or removing these irrelevant or less important attributes reduces the burden on machine learning algorithms. In this work two of the prominent dimensionality reduction techniques, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are investigated on four popular Machine Learning (ML) algorithms, Decision Tree Induction, Support Vector Machine (SVM), Naive Bayes Classifier and Random Forest Classifier using publicly available Cardiotocography (CTG) dataset from University of California and Irvine Machine Learning Repository. The experimentation results prove that PCA outperforms LDA in all the measures. Also, the performance of the classifiers, Decision Tree, Random Forest examined is not affected much by using PCA and LDA.To further analyze the performance of PCA and LDA the eperimentation is carried out on Diabetic Retinopathy (DR) and Intrusion Detection System (IDS) datasets. Experimentation results prove that ML algorithms with PCA produce better results when dimensionality of the datasets is high. When dimensionality of datasets is low it is observed that the ML algorithms without dimensionality reduction yields better results.https://ieeexplore.ieee.org/document/9036908/Cardiotocography datasetdimensionality reductionfeature engineeringlinear discriminant analysismachine learningprincipal component analysis |
| spellingShingle | G. Thippa Reddy M. Praveen Kumar Reddy Kuruva Lakshmanna Rajesh Kaluri Dharmendra Singh Rajput Gautam Srivastava Thar Baker Analysis of Dimensionality Reduction Techniques on Big Data IEEE Access Cardiotocography dataset dimensionality reduction feature engineering linear discriminant analysis machine learning principal component analysis |
| title | Analysis of Dimensionality Reduction Techniques on Big Data |
| title_full | Analysis of Dimensionality Reduction Techniques on Big Data |
| title_fullStr | Analysis of Dimensionality Reduction Techniques on Big Data |
| title_full_unstemmed | Analysis of Dimensionality Reduction Techniques on Big Data |
| title_short | Analysis of Dimensionality Reduction Techniques on Big Data |
| title_sort | analysis of dimensionality reduction techniques on big data |
| topic | Cardiotocography dataset dimensionality reduction feature engineering linear discriminant analysis machine learning principal component analysis |
| url | https://ieeexplore.ieee.org/document/9036908/ |
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