A comprehensive survey on feature selection in the various fields of machine learning

Dhal, Pradip; Azad, Chandrashekhar

doi:10.1007/s10489-021-02550-9

Cited by 172 publications

(70 citation statements)

References 133 publications

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“…-Feature selection: In this process, a subset of features is selected from the health database to be used in the learning process. The feature selection process is done automatically or semi-automatically [64,65]. Decision-making to remove or maintain a feature is based on the desired application.…”

Section: Types Of Data Pre-processing Methodsmentioning

confidence: 99%

“…Then, we select a feature of this subset in each step and remove this feature from the subset. This process continues until the error rate of the learning model decreases [64,65].…”

Section: Types Of Data Pre-processing Methodsmentioning

confidence: 99%

“…* Embedded methods: In these methods, the feature selection process is a component of the learning model. For more details, please refer to [64,65]; * Filtering methods: These methods are considered as an independent part of the learning model. In these methods, a prioritization test is performed on each feature of the database, so these features are ranked based on a specific criterion.…”

Section: Types Of Data Pre-processing Methodsmentioning

confidence: 99%

“…In these methods, a prioritization test is performed on each feature of the database, so these features are ranked based on a specific criterion. Then, the user chooses the superior features [64,65]; -Feature extraction: These methods are used for compressing health data that have high dimensions [66]. This maintains the main features of the database and removes its noise and correlations.…”

Section: Types Of Data Pre-processing Methodsmentioning

confidence: 99%

“…Therefore, in data pre-processing, we may need to filter outliers or estimate missing values. If data also have high dimensions, some data reduction methods, such as feature selection [64,65] or feature extraction [66], can be used. Feature selection selects the best subset of features.…”

Section: The General Framework For Designing a Learning Model In Medicinementioning

confidence: 99%

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Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani

Yousefpoor

et al. 2021

Mathematics

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Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions.

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Section: Types Of Data Pre-processing Methodsmentioning

confidence: 99%

“…Then, we select a feature of this subset in each step and remove this feature from the subset. This process continues until the error rate of the learning model decreases [64,65].…”

Section: Types Of Data Pre-processing Methodsmentioning

confidence: 99%

Section: Types Of Data Pre-processing Methodsmentioning

confidence: 99%

Section: Types Of Data Pre-processing Methodsmentioning

confidence: 99%

Section: The General Framework For Designing a Learning Model In Medicinementioning

confidence: 99%

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Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani

Yousefpoor

et al. 2021

Mathematics

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A many objective based feature selection model for software defect prediction

Mao,

Zhang,

Zhao

et al. 2024

Concurrency and Computation

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SummaryGiven the escalating magnitude and intricacy of software systems, software measurement data often contains irrelevant and redundant features, resulting in significant resource and storage requirements for software defect prediction (SDP). Feature selection (FS) has a vital impact on the initial data preparation phase of SDP. Nonetheless, existing FS methods suffer from issues such as insignificant dimensionality reduction, low accuracy in classifying chosen optimal feature sets, and neglect of complex interactions and dependencies between defect data and features as well as between features and classes. To tackle the aforementioned problems, this paper proposes a many‐objective SDPFS (MOSDPFS) model and the binary many‐objective PSO algorithm with adaptive enhanced selection strategy (BMaOPSO‐AR2) is proposed within this paper. MOSDPFS selects F1 score, the number of features within subsets, and correlation and redundancy measures based on mutual information (MI) as optimization objectives. BMaOPSO‐AR2 constructs a binary version of MaOPSO using transfer functions specifically for binary classification. Adaptive update formulas and the introduction of the R2 indicator are employed to augment the variety and convergence of algorithm. Additionally, performance of MOSDPFS and BMaOPSO‐AR2 are tested on the NASA‐MDP and PROMISE datasets. Numerical results prove that a proposed model and algorithm effectively reduces feature count while enhancing predictive accuracy and minimizing model complexity.

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Evaluation of machine learning models for cytochrome P450 3A4, 2D6, and 2C9 inhibition

Gong,

Feng,

Zhu

et al. 2024

J of Applied Toxicology

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Cytochrome P450 (CYP) enzymes are involved in the metabolism of approximately 75% of marketed drugs. Inhibition of the major drug‐metabolizing P450s could alter drug metabolism and lead to undesirable drug–drug interactions. Therefore, it is of great significance to explore the inhibition of P450s in drug discovery. Currently, machine learning including deep learning algorithms has been widely used for constructing in silico models for the prediction of P450 inhibition. These models exhibited varying predictive performance depending on the use of machine learning algorithms and molecular representations. This leads to the difficulty in the selection of appropriate models for practical use. In this study, we systematically evaluated the conventional machine learning and deep learning models for three major P450 enzymes, CYP3A4, CYP2D6, and CYP2C9 from several perspectives, such as algorithms, molecular representation, and data partitioning strategies. Our results showed that the XGBoost and CatBoost algorithms coupled with the combined fingerprint/physicochemical descriptor features exhibited the best performance with Area Under Curve (AUC) of 0.92, while the deep learning models were generally inferior to the conventional machine learning models (average AUC reached 0.89) on the same test sets. We also found that data volume and sampling strategy had a minor effect on model performance. We anticipate that these results are helpful for the selection of molecular representations and machine learning/deep learning algorithms in the P450 model construction and the future model development of P450 inhibition.

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A comprehensive survey on feature selection in the various fields of machine learning

Cited by 172 publications

References 133 publications

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

A many objective based feature selection model for software defect prediction

Evaluation of machine learning models for cytochrome P450 3A4, 2D6, and 2C9 inhibition

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