Automating feature engineering in machine Learning
Feature engineering is a vital part of machine learning that transforms massive raw data into the applicable feature set, and many algorithms of feature engineering have been proposed to promote the efficiency and accuracy of this process. Moreover, automated feature engineering has also been resear...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/141857 |
| _version_ | 1824454464735543296 |
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| author | Zhu, Haoyu |
| author2 | Mao Kezhi |
| author_facet | Mao Kezhi Zhu, Haoyu |
| author_sort | Zhu, Haoyu |
| collection | NTU |
| description | Feature engineering is a vital part of machine learning that transforms massive raw data into the applicable feature set, and many algorithms of feature engineering have been proposed to promote the efficiency and accuracy of this process. Moreover, automated feature engineering has also been researched. For example, the AutoLearn feature learning algorithm automatedly generates new features by the linear relationship among features and then performs feature selection process on the new feature set [1]. This project is focusing on feature engineering, specifically feature selection, to develop a unified function that integrates different types of feature selection methods for the automation by hyperparameter optimization. The goals of this project are to utilize a unified function that can implement all types of feature selection algorithms and to automate this process as many hyperparameters could be tuned automatically. And explorations on feature selection algorithms and classification models were carried for designing the unified function. Automated feature selection was also tested on wrapper methods by utilizing the Bayesian optimization method. As the deliverable of this project, the unified function was capable of implementing any kind of feature selection algorithm and outputting feature set that potentially can improve the classification performance. Experiments using the unified function on different datasets showed the potentials to eradicate manual efforts in selecting a subset of useful features. |
| first_indexed | 2025-02-19T03:22:44Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/141857 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:22:44Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1418572023-07-07T17:46:17Z Automating feature engineering in machine Learning Zhu, Haoyu Mao Kezhi School of Electrical and Electronic Engineering Agency for Science, Technology and Research (A*STAR) Yang Feng EKZMao@ntu.edu.sg Engineering::Electrical and electronic engineering Feature engineering is a vital part of machine learning that transforms massive raw data into the applicable feature set, and many algorithms of feature engineering have been proposed to promote the efficiency and accuracy of this process. Moreover, automated feature engineering has also been researched. For example, the AutoLearn feature learning algorithm automatedly generates new features by the linear relationship among features and then performs feature selection process on the new feature set [1]. This project is focusing on feature engineering, specifically feature selection, to develop a unified function that integrates different types of feature selection methods for the automation by hyperparameter optimization. The goals of this project are to utilize a unified function that can implement all types of feature selection algorithms and to automate this process as many hyperparameters could be tuned automatically. And explorations on feature selection algorithms and classification models were carried for designing the unified function. Automated feature selection was also tested on wrapper methods by utilizing the Bayesian optimization method. As the deliverable of this project, the unified function was capable of implementing any kind of feature selection algorithm and outputting feature set that potentially can improve the classification performance. Experiments using the unified function on different datasets showed the potentials to eradicate manual efforts in selecting a subset of useful features. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-06-11T05:24:30Z 2020-06-11T05:24:30Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/141857 en B1083-191 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Zhu, Haoyu Automating feature engineering in machine Learning |
| title | Automating feature engineering in machine Learning |
| title_full | Automating feature engineering in machine Learning |
| title_fullStr | Automating feature engineering in machine Learning |
| title_full_unstemmed | Automating feature engineering in machine Learning |
| title_short | Automating feature engineering in machine Learning |
| title_sort | automating feature engineering in machine learning |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/141857 |
| work_keys_str_mv | AT zhuhaoyu automatingfeatureengineeringinmachinelearning |