Machine Learning Techniques for Classification of Stress Levels in Traffic by Amanda Trojan Fenerich, Egídio José Romanelli, Rodrigo Eduardo Catai, Maria Teresinha Arns Steiner

“… The aim of this study is to apply Machine Learning techniques for predicting and classifying the stress level of people commuting from home to work and also to evaluate the performance of prediction models using feature selection. The database was obtained through a structured questionnaire with 44 questions, applied to 196 people in the city of Curitiba, PR. …”
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Penyeimbangan Kelas SMOTE dan Seleksi Fitur Ensemble Filter pada Support Vector Machine untuk Klasifikasi Penyakit Liver by Muhammad Amir Nugraha, Muhammad Itqan Mazdadi, Andi Farmadi, Muliadi, Triando Hamonangan Saragih

“…The test can prove if SMOTE on class balancing and Ensemble Filter on feature selection can improve the classification performance of the SVM method.…”
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Approach for Text Classification Based on the Similarity Measurement between Normal Cloud Models by Jin Dai, Xin Liu

“…By the comparison among different text classifiers in different feature selection set, it fully proves that not only does CCJU-TC have a strong ability to adapt to the different text features, but also the classification performance is also better than the traditional classifiers.…”
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Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis. by Mwenge Mulenga, Arutchelvan Rajamanikam, Suresh Kumar, Saharuddin Bin Muhammad, Subha Bhassu, Chandramathi Samudid, Aznul Qalid Md Sabri, Manjeevan Seera, Christopher Ifeanyi Eke

“…This paper introduces a novel feature engineering method that circumvents these limitations by amalgamating two feature sets derived from input data to generate a new dataset, which is then subjected to feature selection. This innovative approach markedly enhances the Area Under the Curve (AUC) performance of the Deep Neural Network (DNN) algorithm in colorectal cancer (CRC) detection using gut microbiome data, elevating it from 0.800 to 0.923. …”
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Model-Based Sensitivity Analysis on Aerosol Optical Thickness Prediction by Bo Han, Xiaowei Gao, Xiaohui Cui

“…Next, the attribute sensitivity orders are used for feature selection in the context of regression by removing insensitive attribute one at a time or by removing attributes whose sensitive orders are larger than number k . …”
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A New Rule-Based Classification Method Using Shape-Based Properties of Spectral Curves by Songuel Polat, Alain Tremeau, Frank Boochs

“…The effective handling and use of such datasets for classification requires processing steps (dimensionality reduction through feature selection or feature extraction) that are not always goal-oriented. …”
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Compressed Sensing Based Fingerprint Identification for Wireless Transmitters by Caidan Zhao, Xiongpeng Wu, Lianfen Huang, Yan Yao, Yao-Chung Chang

“…Complex analytical wavelet transform is used to obtain the envelope of the transient signal, and the corresponding features are extracted by using the compressed sensing theory. Feature selection utilizing minimum redundancy maximum relevance (mRMR) is employed to obtain the optimal feature subsets for identification. …”
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A Hierarchical Framework Approach for Voice Activity Detection and Speech Enhancement by Yan Zhang, Zhen-min Tang, Yan-ping Li, Yang Luo

“…The modified Wiener filter (MWF) approach is utilized for noise reduction in the speech enhancement block. For the feature selection and voting block, several discriminating features were employed in a voting paradigm for the consideration of reliability and discriminative power. …”
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Analyzing the Application of Machine Learning in Anemia Prediction by Li Yuxi

“…Obstacles such as data quality, feature selection, and model interpretability continue to hinder clinical adoption. …”
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Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques by Jaweria Kainat, Syed Sajid Ullah, Fahd S. Alharithi, Roobaea Alroobaea, Saddam Hussain, Shah Nazir

“…These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. …”
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Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies by Paul Bosch, Mauricio Herrera, Julio López, Sebastián Maldonado

“…The proposed methodology is based on using well-established procedures of feature selection, which were used to determine a suitable brain functional network size related to math problem solving strategies and also to discover the most relevant links in this network without including noisy connections or excluding significant connections.…”
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A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods by Jaehee Jung, Heung Ki Lee, Gangman Yi

“…To define the relationship between IPR and GO terms, three pattern recognition techniques have been employed under different conditions, such as feature selection and weighted value, instead of a binary one.…”
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Application research on the time–frequency analysis method in the quality detection of ultrasonic wire bonding by Wuwei Feng, Xin Chen, Cuizhu Wang, Yuzhou Shi

“…Then, the principal component analysis method was further used for feature selection. Finally, an artificial neural network was built to recognize and detect the quality of ultrasonic wire bonding. …”
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Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model by Akshat Gaurav, Brij B. Gupta, Razaz Waheeb Attar, Ahmed Alhomoud, Varsha Arya, Kwok Tai Chui

“…In this context, this work suggests a new method to find drivers by means of a Random Forest model optimized using the osprey optimization algorithm (OOA) for feature selection and the salp swarm optimization (SSO) for hyperparameter tuning based on driving behavior. …”
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Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology by J. M. Urquiza, I. Rojas, H. Pomares, J. Herrera, J. P. Florido, O. Valenzuela

“…In the present work, a new approach is proposed to construct a PPI predictor training a support vector machine model through a mutual information filter-wrapper parallel feature selection algorithm and an iterative and hierarchical clustering to select a relevance negative training set. …”
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A Malware Detection Scheme Based on Mining Format Information by Jinrong Bai, Junfeng Wang, Guozhong Zou

“…Based on in-depth analysis of the static format information of the PE files, we extracted 197 features from format information of PE files and applied feature selection methods to reduce the dimensionality of the features and achieve acceptable high performance. …”
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Time Effort Prediction Of Agile Software Development Using Machine Learning Techniques by Muchamad Bachram Shidiq, Windu Gata, Sigit Kurniawan, Dedi Dwi Saputra, Supriadi Panggabean

“…For this reason, this research aims to predict the time effort of agile software development using Machine Learning techniques, namely the Decision Tree, Random Forest, Gradient Boosting, and AdaBoost algorithms, as well as the use of feature selection in the form of RRelieff and Principal Component Analysis (PCA) to improve prediction accuracy. …”
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Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic. by Nouman Butt, Muhammad Munwar Iqbal, Shabana Ramzan, Ali Raza, Laith Abualigah, Norma Latif Fitriyani, Yeonghyeon Gu, Muhammad Syafrudin

“…To address these issues, this research examines the implementation of an automated disease classification system using deep learning and optimal feature selection. The system incorporates data augmentation and transfer learning with pre-trained models such as DenseNet-201 and AlexNet to improve diagnostic accuracy, efficiency, and cost-effectiveness. …”
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Modeling of Human Skin by the Use of Deep Learning by Xin Xiong, Xuexun Guo, Yiping Wang

“…Applying good classifier with best feature selection achieved good result in terms of accuracy, 95%, and recognition rate, 93%. …”
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Quantitative Analysis of Comprehensive Influence of Music Network Based on Logistic Regression and Bidirectional Clustering by Yi-Kun Zhao, Guo-Qing Wang, Xiao-Xiao Zhan, Peng-Hui Yang

“…Finally, the lasso region is used for feature selection to obtain the change factors in the process of music evolution and analyze the dynamic changes in the process of music development. …”
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