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A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification
Sensors, 2021 In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform
Moumita Mandal +4 more
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A multiple filter-wrapper feature selection algorithm based on process optimization mechanism for high-dimensional omics data analysis. [PDF]
PLoS ONERecently, hybrid feature selection methods have demonstrated excellent performance on high-dimensional data, but many of these methods tend to yield relatively homogeneous feature subsets.
Yongtao Shi, Yuefeng Zheng, Xiaotong Bai
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A wrapper method for feature selection using Support Vector Machines
Information Sciences, 2009 We introduce a novel wrapper Algorithm for Feature Selection, using Support Vector Machines with kernel functions. Our method is based on a sequential backward selection, using the number of errors in a validation subset as the measure to decide which feature to remove in each iteration.
Sebastian Maldonado, Richard Weber
exaly +4 more sources
Breast Cancer Prediction Using Rotation Forest Algorithm Along with Finding the Influential Causes [PDF]
BioengineeringBreast cancer is a widespread disease involving abnormal (uncontrolled) growth of breast tissue cells along with the formation of a tumor and metastasis. Breast cancer cases occur mostly among women.
Prosenjit Das +3 more
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An improved wrapper-based feature selection method for machinery fault diagnosis. [PDF]
PLoS ONE, 2017 A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis.
Kar Hoou Hui +4 more
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Swag: A Wrapper Method for Sparse Learning [PDF]
SSRN Electronic Journal, 2020 Predictive power has always been the main research focus of learning algorithms with the goal of minimizing the test error for supervised classification and regression problems. While the general approach for these algorithms is to consider all possible attributes in a dataset to best predict the response of interest, an important branch of research is
Roberto Molinari +5 more
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Computation, 2021 The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and ...
Zenab Mohamed Elgamal +5 more
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Wrapper methods for multi-objective feature selection
26th International Conference on Extending Database Technology, 2023 The ongoing data boom has democratized the use of data for improved decision-making. Beyond gathering voluminous data, preprocessing the data is crucial to ensure that their most rele- vant aspects are considered during the analysis. Feature Selection (FS) is one integral step in data preprocessing for reducing data dimensionality and preserving the ...
Njoku, Uchechukwu Fortune +3 more
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Accuracy Improvement in Software Cost Estimation based on Selection of Relevant Features of Homogeneous Clusters [PDF]
Journal of Artificial Intelligence and Data Mining, 2023 Software Cost Estimation (SCE) is one of the most widely used and effective activities in project management. In machine learning methods, some features have adverse effects on accuracy.
Saba Beiranvand +1 more
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Establishment of Near-infrared Prediction Model for Seven Chemical Components of Wrapper Tobacco
Guangdong nongye kexue, 2023 【Objective】It is complex, time-consuming and laborious for conventional chemical detection method to detect the content of total nitrogen, potassium, total sugars, reducing sugars, total alkali, chlorine and magnesium in wrapper tobacco, while near ...
Hongjian LIU +7 more
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