Proceedings Article10.1109/SMC42975.2020.9282969
Feature Selection with Dynamic Classifier Ensembles
Hakan Ezgi Kiziloz,Ayça Deniz +1 more
- 11 Oct 2020
- pp 2038-2043
4
TL;DR: A new multiobjective selection model that dynamically searches for the best ensemble of five classifiers to extract the best representative feature subsets is proposed and shows that the proposed method performs significantly better than all the machine learning techniques when they are executed separately.
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Abstract: With the advance in technology, the volume of available data grows massively. Therefore, feature selection has become an essential preprocessing step to extract valuable information. Feature selection is the task of reducing the number of features by removing redundant features from data while preserving the classification accuracy. It is a multiobjective problem as there are two objectives. In general, multiobjective selection algorithms with machine learning techniques are utilized to find the most promising feature subsets; however, classification performances of these machine learning techniques are analyzed separately. In this study, we propose a new multiobjective selection model that dynamically searches for the best ensemble of five classifiers to extract the best representative feature subsets. We present the experiment results on 12 well-known datasets. The results show that the proposed method performs significantly better than all the machine learning techniques when they are executed separately. Moreover, the proposed method outperforms two existing ensemble algorithms, namely AdaBoost and Gradient Boosting.
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Citations
Multimodal Bare-Bone Niching Differential Evolution in Feature Selection.
Xiao-Min Hu,Zi-Wen Guo +1 more
TL;DR: This paper proposes MBNDE, a multimodal bare-bone niching differential evolution algorithm for feature selection in classification problems, achieving high classification rates through multiple feature combinations and outperforming existing algorithms in FS tasks.
Exploring Different Feature Selection Techniques for High Risk Disease prediction using Machine Learning
Prema S. Kadam,Sachin P. Godse +1 more
- 25 Apr 2024
TL;DR: This review gives detailed information about all different methods which are applied in different application area to select the most important features and removes redundant or irrelevant features.
An ensemble learning-based feature selection algorithm for identification of biomarkers of renal cell carcinoma
Zekun Xin,Ruhong Lv,Wei Liu,Shenghan Wang,Qiang Gao,Bao Zhang,Guangyu Sun +6 more
TL;DR: A three-stage feature selection algorithm framework for high-dimensional data based on ensemble learning (EFS-GINI) is proposed and m5C-related genes play an important role in the occurrence and progression of renal cell carcinoma, and are expected to become an important marker to predict the prognosis of patients.
Effective Analysis of Heart Disease Prediction using Machine Learning Techniques
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TL;DR: In this article , machine learning techniques like Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), KNN, Support Vector Machine, and XGBoost were used to detect heart disease throughout.
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Feature Selection: A Data Perspective
TL;DR: Feature selection, as a data preprocessing strategy, has proven to be effective and efficient in preparing data (especially high-dimensional data) for various data mining and machine learning problems.
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Feature Selection: A Data Perspective
TL;DR: This survey revisits feature selection research from a data perspective and reviews representative feature selection algorithms for conventional data, structured data, heterogeneous data and streaming data, and categorizes them into four main groups: similarity- based, information-theoretical-based, sparse-learning-based and statistical-based.
A Survey on Evolutionary Computation Approaches to Feature Selection
TL;DR: This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms.
Ensembles for feature selection: A review and future trends
TL;DR: This work provides the reader with the basic concepts necessary to build an ensemble for feature selection, as well as reviewing the up-to-date advances and commenting on the future trends that are still to be faced.
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