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Scalable extensions of the relieff algorithm for weighting and selecting features on the multi-label learning context

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NEUROCOMPUTING 2015年 161卷 168-182页

作者： Reyes, Oscar Morell, Carlos Ventura, Sebastian Univ Holguin Dept Comp Sci Holguin Cuba Univ Cent Las Villas Dept Comp Sci Santa Clara Cuba Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain King Abdulaziz Univ Dept Comp Sci Jeddah 21413 Saudi Arabia

Multi-label learning has become an important area of research due to the increasing number of modern applications that contain multi-label data. The multi-label data are structured in a more complex way than single-label data. Consequently the development of techniques that allow the improvement in the performance of machine learning algorithms over multi-label data is desired. The feature weighting and feature selection algorithms are important feature engineering techniques which have a beneficial impact on the machine learning. The relieff algorithm is one of the most popular algorithms to feature estimation and it has proved its usefulness in several domains. This paper presents three extensions of the relieff algorithm for working in the multi-label learning context, namely relieff-ML, PPT-relieff and Rrelieff-ML. PPT-relieff uses a problem transformation method to convert the multi-label problem into a single-label problem. relieff-ML and Rrelieff-ML adapt the classic relieff algorithm in order to handle directly the multi-label data. The proposed relieff extensions are evaluated and compared with previous relieff extensions on 34 multi-label datasets. The results show that the proposed relieff extensions improve preceding extensions and overcome some of their drawbacks. The experimental results are validated using several nonparametric statistical tests and confirm the effectiveness of the proposal for a better multi-label learning. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi-label learning relieff algorithm Feature weighting Feature selection Multi-label classification Label ranking

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Hybrid Model Based on relieff algorithm and K-Nearest Neighbor for Erythemato-Squamous Diseases Forecasting

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第2期47卷 1299-1307页

作者： Alotaibi, Abdullah S. Shaqra Univ Shaqra Saudi Arabia

Machine learning (ML) techniques have been used to solve real-world problems for decades. In the field of medical sciences, these approaches have been found to be useful in the diagnosis and prognosis of a variety of medical disorders. However, when dealing with voluminous, inconsistent, and higher-dimensional data, conventional ML approaches have failed to deliver the expected results. Researchers have suggested hybrid solutions to resolve these problems, which have been found to be more effective than conventional methods because these systems integrate their merits while reducing their drawbacks. In the current research article, hybrid model has been presented by coupling feature optimization with prediction approach. The proposed hybrid model has two stages: the first involves implementing the relieff algorithm for optimum feature selection in erythemato-squamous diseases, and the second involves implementing k-nearest neighbor (KNN) for prediction of those selected optimum features. The experimentation was carried out on bench mark dataset for erythemato-squamous diseases. The presented hybrid model was also assessed with conventional KNN approach based on various metrics such as classification accuracy, kappa coefficient, recall, precision, and f-score.

关键词： KNN relieff algorithm Dermatology Medical diagnosis Machine learning

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APPLICATION OF relieff algorithm TO SELECTING FEATURE SETS FOR CLASSIFICATION OF HIGH RESOLUTION REMOTE SENSING IMAGE 36

APPLICATION OF RELIEFF ALGORITHM TO SELECTING FEATURE SETS F...

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36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Wang, Zhi Zhang, Yan Chen, Zhichao Yang, Huan Sun, Yaxin Kang, Jianmin Yang, Yong Liang, Xiaojun Northeastern Univ Coll Resources & Civil Engn Inst Geoinformat & Digital Mine Res Shenyang 110819 Peoples R China Anshan Iron Steel Grp Min Co Ltd East Anshan Iron Mine Anshan 114041 Peoples R China

ISBN: (纸本)9781509033324

In classification, a large number of features often make it difficult to select appropriate classification features. In such situations, feature selection or dimensionality reduction methods play an important role in classification. relieff algorithm is one of the most successful filtering feature selection methods. In this paper, some shortcomings of the relieff algorithm are improved, on the problem of poor stability of neighbor samples selection, proposing the method of using the average value of multiple random selection to improve the anti-volatility of the algorithm. And redundant analysis is added to the relieff algorithm to eliminate the redundant features. The experimental results show that the improved relieff algorithm can effectively establish the classification feature sets, achieve the better classification accuracy.

关键词： classification feature selection relieff algorithm

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Laser-Induced Breakdown Spectroscopy for the Discrimination of Explosives Based on the relieff algorithm and Support Vector Machines

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FRONTIERS IN PHYSICS 2021年 9卷

作者： Zhao, Yu Wang, Q. Q. Cui, Xutai Teng, Geer Wei, Kai Liu, Haida Beijing Inst Technol Sch Opt & Photon Beijing Peoples R China North Univ China Sch Informat & Commun Engn Taiyuan Peoples R China

Real-time explosive detectors must be developed to facilitate the rapid implementation of appropriate protective measures against terrorism. We report a simple yet efficient methodology to classify three explosives and three non-explosives by using laser-induced breakdown spectroscopy. However, the similarity existing among the spectral emissions collected from the explosives resulted in the difficulty of separating samples. We calculated the weights of lines by using the relieff algorithm and then selected six line regions that could be identified from the arrangement of weights to calculate the area of each line region. A multivariate statistical method involving support vector machines was followed for the construction of the classification model. Several models were constructed using full spectra, 13 lines, and 100 lines selected by the arrangement of weights and areas of the selected line regions. The highest correct classification rate of the model reached 100% by using the six line regions.

关键词： LIBS explosives line region relieff algorithm support vector machines classification

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Evolutionary LightGBM-Based Intrusion Detection System for IoT Networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第5期38卷

作者： Singal, Khushi Kandhoul, Nisha Dhurander, Sanjay K. Netaji Subhas Univ Technol Dept Informat Technol New Delhi India Natl Inst Elect & Informat Technol New Delhi India

With the rapid growth of the Internet of Things (IoT), securing interconnected devices is becoming increasingly critical. This paper introduces the LightShield intrusion detection system (IDS) to enhance intrusion detection in IoT environments using high-performance computing. LightShield features preprocessing of IoT data, relieff algorithm for feature selection, and a novel detection model based on LightGBM, a gradient boosting framework. The system leverages GPU acceleration for faster model validation, enabling real-time monitoring. By adapting to IoT characteristics, LightShield provides flexible, scalable defense against evolving cyber threats. Results show its potential to improve security in IoT ecosystems, offering valuable insights into anomaly-based intrusion detection and the future of secure IoT networks. The binary classification model displayed exceptional precision with a 99.82% accuracy in detecting potential attacks, and the multiclass classification model achieved a commendable 97.25% accuracy in classifying distinct attack types.

关键词： anomaly detection intrusion detection internet of things (IoT) LightShield IDS relieff algorithm security

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Evaluating the potential of airborne hyperspectral imagery in monitoring common beans with common bacterial blight at different infection stages

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BIOSYSTEMS ENGINEERING 2025年 251卷 145-158页

作者： Jing, Binghan Wang, Jiachen Zhang, Xin Hou, Xiaoxiang Huang, Kunming Wang, Qianyu Wang, Yiwei Jia, Yaoxuan Feng, Meichen Yang, Wude Wang, Chao Shanxi Agr Univ Coll Agr Taigu 030801 Peoples R China

Common bacterial blight (CBB) is the most destructive bacterial disease affecting the production of common beans, and timely detection of CBB is crucial to limiting its spread. In this study, correlation analysis and the relieff algorithm were used to select vegetation indices (VIs) and texture features (TFs) that are sensitive to CBB. The CBB monitoring model based on support vector machine regression (SVR), random forest regression (RFR), and K-nearest neighbor regression (KNNR) was established using the selected the VIs, TFs, and their combinations. Then, the impact of the spatial resolution on the disease monitoring accuracy was evaluated. In addition, the early infection monitoring model was further optimised. The results show that in the early infection stage, when the spatial resolution was 0.07 m, the window size was 7 x 7, and the independent variable was a combination of VIs and TFs, the R2 of the monitoring model constructed via SVR was 0.72, which was 14.3% higher than that obtained for a 3 x 3 window (0.63). In the middle and late infection stages, the optimal spatial resolution was 0.1 m, and the monitoring model constructed using RFR and a combination of VIs and TFs performed the best, with R2 values of 0.81 and 0.88, respectively. The research results indicate that selecting an appropriate spatial resolution and window size can effectively improve the model's CBB monitoring ability and can provide a reference for accurate monitoring of large-scale CBB of common beans using airborne or spaceborne imaging spectroscopy technology.

关键词： Legume diseases relieff algorithm Machine learning Spatial resolution Window size

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Improved firefly algorithm for feature selection with the relieff-based initialization and the weighted voting mechanism

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NEURAL COMPUTING & APPLICATIONS 2023年第1期35卷 275-301页

作者： Yong, Xin Gao, Yue-lin North Minzu Univ Sch Comp Sci & Engn Wenchang North St Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Ningxia Prov Key Lab Intelligent Informat & Data Wenchang North St Yinchuan 750021 Ningxia Peoples R China

Feature selection has become popular in data mining tasks currently for its ability of improving the performance of the algorithm and gaining more information about the dataset. Although the firefly algorithm is a well-performed heuristic algorithm, there is still much room for improvement as to the feature selection problem. In this research, an improved firefly algorithm designed for feature selection with the relieff-based initialization method and the weighted voting mechanism is proposed. First of all, a feature grouping initialization method that combines the results of the relieff algorithm and the cosine similarity is designed to take place of random initialization. Then, the direction of the firefly is modified to move toward the optimal solution. Finally, inspired by the ensemble algorithm, a weighted voter is proposed to build recommended positions for fireflies, which is also integrated with the elite crossover operator and the mutation operator to improve the diversity of the population. Selected from the mixed swarm, a new population is constructed to replace the original population in the next stage. To verify the effectiveness of the algorithm proposed in this paper, 18 datasets are utilized and 9 comparison algorithms (e.g., Black Hole algorithm, Grey Wolf Optimizer and Pigeon Inspired Optimizer) from state-of-the-art related works are selected for the simulating experiments. The experimental results demonstrate the superiority of the proposed algorithm applied to the feature selection problem.

关键词： Firefly algorithm Feature selection Feature grouping relieff algorithm Weighted voting

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Enhancing E-Nose Performance via Metal-Oxide Based MEMS Sensor Arrays Optimization and Feature Alignment for Drug Classification

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SENSORS 2025年第5期25卷 1480-1480页

作者： Kong, Ruiwen Shen, Wenfeng Gao, Yang Lv, Dawu Ai, Ling Song, Weijie Tan, Ruiqin Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China Chinese Acad Sci Ningbo Inst Mat Technol & Engn Zhejiang Prov Engn Res Ctr Energy Optoelect Mat & Ningbo 315201 Peoples R China CS Microsensor Ningbo Technol Co Ltd Ningbo 311121 Peoples R China Res & Mfg Base Olfactory Sensors China Sensing Val Bengbu 233040 Peoples R China Hangzhou Dianzi Univ Coll Integrated Circuit Sci & Engn Shanghai 201713 Peoples R China

This article introduces a novel approach to improve electronic nose classification accuracy by optimizing sensor arrays and aligning features. This involves selecting the best sensor combinations and reducing redundant information for better odor recognition. We employ a feature alignment algorithm to address the discrepancies that impede model sharing between electronic nose devices. Our research focuses on overcoming challenges associated with material selection and the constraints of transferring classification models across different electronic nose devices for drug classification. We fabricated six SnO2-based MEMS gas sensors using physical vapor deposition. The relieff algorithm was employed to rank and score each sensor's contribution to drug classification, identifying the optimal sensor array. We then applied feature alignment from transfer learning to enhance model sharing among three inconsistent devices. This study resolves the issue of electronic noses being hard to use on the same database due to hardware inconsistencies in batch production, laying the groundwork for future mass production.

关键词： electronic nose MEMS transfer learning relieff algorithm feature alignment

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Research on application of relieff and improved RVM in water quality grade evaluation

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WATER SCIENCE AND TECHNOLOGY 2022年第3期85卷 799-810页

作者： Gong, Yu-Lin Hu, Ming-Jia Yang, Hui-Fang Han, Bo Changchun Univ Sci & Technol Sch Elect Informat Engn Changchun Jilin Peoples R China

relieff algorithm was used to analyze the weight of each water quality evaluation factor, and then based on the Relevance Vector Machine (RVM), Particle Swarm Optimization (PSO) was used to optimize the kernel width factor and hyperparameters of RVM to build a water quality evaluation model, and the experimental results of RVM, PSO-RVM, relieff-RVM and PSO-relieff-RVM were compared. The results show that relieff algorithm, combined with threshold value, selects 5 evaluation factors with significant weight from 8 evaluation factors, which reduces the amount of data used in the model, CSI index is used to calculate the separability of each evaluation factor combination. The results show that the overall separability of the combination is best when the evaluation factor with significant weight is reserved. When different water quality evaluation factors were included, the evaluation accuracy of PSO-relieff-RVM model reached 95.74%, 14.23% higher than that of RVM model, which verified the effectiveness of PSO algorithm and relieff algorithm, and had a higher guiding significance for the study of water quality grade evaluation. It has good practical application value.

关键词： particle swarm optimization relevance vector machine relieff algorithm water quality grade evaluation

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A Novel Process Monitoring Method Based on Dynamic Related relieff-SFA Method

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IEEE ACCESS 2020年 8卷 41673-41683页

作者： Liu, Xiaozhi Zhang, Yue Zhang, Liping Yang, Yinghua Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

This paper proposes a condition monitoring method based on Dynamic Related relieff-SFA (DRRSFA), which solves the problem of huge variable dimensions and strong autocorrelation in process monitoring. First, the samples are mapped into a new space, and the slow projection components (SPCs) of the samples are calculated. The SPCs not only considers the autocorrelation between the variables, but also characterizes the inherent properties of the whole system. Second, the feature selection algorithm relieff is used to select the more weighted features, which is called the principal components(PCs) to achieve dimensionality reduction. Then the corresponding statistics and control limits are calculated based on the obtained PCs. Finally, the process monitoring using the proposed algorithm is performed by testing a numerical example and the actual production process data, and the results show the effectiveness of the proposed method.

关键词： Feature extraction Correlation Monitoring Principal component analysis Fault diagnosis Classification algorithms Data mining Process monitoring slow feature analysis slow projection components relieff algorithm projection components

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