Variable selection based on maximum information coefficient for data modeling

Chu, Fuchang; Zhen-ping, Fan; Guo, Baohui; Zhi, Dan; Yin, Zijian; Zhao, Wenyi

doi:10.1109/iaeac.2017.8054306

Cited by 9 publications

(1 citation statement)

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“…The filtering method uses each feature's mathematical and theoretical properties to select parts which can reflect the correlation between features and labels. The maximum information coefficient (MIC) [24] has a strong universality, It can identify not only the linear and nonlinear relations between the characteristic variables, but also the non-functional relations between the characteristic variables, and has a good recognition effect on complex acoustic features. Therefore, MIC is used in the filtering method; the packing method consists of the search strategy and the learning algorithm.…”

Section: Hfs Algorithmsmentioning

confidence: 99%

APVC2021-A Gas Leakage Detection Method with Hybrid Acoustic Feature Selection and Stacking Ensemble Learning

et al. 2023

Preprint

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Model’s generalization and feature selection are always two challenging problems for gas leakage detection. This paper presents a method for gas leakage detection based on Hybrid-Feature-Selection-Stacking ensemble learning (HFS-Stacking), which fuses the Hybrid Feature Selection and Stacking ensemble learning. Firstly, a hybrid feature selection algorithm (HFS), which combines SFS-SVM, SFS-KNN, RFE-RF, RFE-XGB, and MIC algorithms, is proposed to select the optimal feature subset from multiple commonly used acoustic signal features; Then, SVM, KNN, random forest and XGBoost are designed as base learners in the stacking integration framework, which increase the generalization ability of the model. XGBoost is used as the meta-learner to output the classification results. The proposed gas leakage detection method gets the optimal subset of features and speeds of the subsequent model. Meanwhile, the constructed model can effectively improve the indicator of ,, and, and has a good generalization ability. The experimental results show that the HFS-Stacking algorithm using fewer features can effectively improve the training speed, accuracy, F1-score, AUC value and recall rate, and also has better robustness.

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Section: Hfs Algorithmsmentioning

confidence: 99%

APVC2021-A Gas Leakage Detection Method with Hybrid Acoustic Feature Selection and Stacking Ensemble Learning

et al. 2023

Preprint

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Weighted ReliefF with threshold constraints of feature selection for imbalanced data classification

Song

Dai

et al. 2020

Concurrency and Computation

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SummaryFeature selection is a useful method for fulfilling the data classification since the inherent heterogeneity of data and the redundancy of features are often encountered in the current data exploding era. Some commonly used feature selection algorithms, which include but are not limited to Pearson, maximal information coefficient, and ReliefF, are well‐posed under the assumption that instances are distributed homogenously in datasets. However, such an assumption might be not true in the practice. As such, in the presence of data imbalance, these traditional feature selection algorithms might be invalid due to their prejudices to the minority class, which includes few samples. The purpose of the addressed problem in this article is to develop an effective feature selection algorithm for imbalanced judicial datasets, which is capable of extracting essential features while deleting negligible ones according to the practical feature requirements. To achieve this goal, the number and the distribution of samples in each class are fully taken into consideration for the correlation analysis. Compared with the traditional feature selection algorithms, the proposed improved ReliefF algorithm is equipped with: (i) different weights of features according to the characteristics of heterogeneous samples in different classes; (ii) justice for imbalanced datasets; and (iii) threshold constraints resulting from the practical feature requirements. Finally, experiments on a judicial dataset and six public datasets well illustrate the effectiveness and the superiority of the proposed feature selection algorithm in improving the classification accuracy for imbalanced datasets.

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