Application of Semi-Supervised Clustering with Membership Information and Deep Learning in Landslide Susceptibility Assessment by Hua Xia, Zili Qin, Yuanxin Tong, Yintian Li, Rui Zhang, Hongxia Luo

“…It utilizes landslide and unlabeled samples to map landslide membership degree via Semi-supervised Fuzzy C-Means (SFCM). …”
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A New Bearing Fault Diagnosis Method Based on Refined Composite Multiscale Global Fuzzy Entropy and Self-Organizing Fuzzy Logic Classifier by Zhang Ziying, Zhang Xi

“…Then, the testing sample set is input to the testing stage of the SOF for classification. …”
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Qin Seal Script Character Recognition with Fuzzy and Incomplete Information by Yun Ou, Zhen-Jie Zhou, Di-Wen Kang, Pan Zhou, Xue-Wei Liu

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Evolutionary fuzzy learning for Chinese medicine liver syndrome differentiation by Jia-Yu Yan, Peng-Wei Zhang, Wei-Guo Sheng, Jun-Ping Shi, Wei Ni, Li Li, Yu-Jun Zheng

“…We compared our model with seven popular or state-of-the-art models on a real-world dataset of 11,250 samples (9000 for training and 2250 for test). In terms of regression performance averaged over the six syndromes, our model obtained the mean absolute error of 0.139, mean squared error of 0.055, explained variance score of 0.811, and R2 score of 0.811; in terms of the performance of multi-classification (none, mild, and severe classes of each syndrome), our model obtained the average precision value of 0.936, recall value of 0.96, and F-score of 0.946. …”
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Missing values imputation using Fuzzy K-Top Matching Value by Azza Ali, Mervat Abu-Elkheir, Ahmed Atwan, Mohammed Elmogy

“…Researchers exclude or impute influenced variables and data. This study proposes Fuzzy K-Top Matching Value (FKTM) for missing value imputation. …”
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Designing a fuzzy system for lung disease diagnosis based on spirometry by firuze ebrahimpoor, vali derhami, mehrdad mostaghaci, mohammad javad zare, Raziyeh Soltani Gerdefaramarzi

“…Result: Among the used classification methods, the fuzzy method was achieved acceptable results because of the samples awarded degree in each cluster.According to the developed model in this study for predicting obstructive and restrictive pulmonary syndrome based on the analyzed test samples related to information of workers in 1394, the achieved accuracy was 0.71and 0.79, respectively. …”
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DLPLSR: Dual Label Propagation-Driven Least Squares Regression with Feature Selection for Semi-Supervised Learning by Shuanghao Zhang, Zhengtong Yang, Zhaoyin Shi

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Fuzzy x- and s Control Charts: A Data-Adaptability and Human-Acceptance Approach by Ming-Hung Shu, Dinh-Chien Dang, Thanh-Lam Nguyen, Bi-Min Hsu, Ngoc-Son Phan

“…Therefore, for well accommodating this fuzzy-data domain, this paper integrates fuzzy set theories to establish the fuzzy charts under a general variable-sample-size condition. …”
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AI-driven UAV with image processing algorithm for automatic visual inspection of aircraft external surface by Mohammed A. H. Ali, Muhammad Zamil A. Zulkifle, Nik Nazri Nik Ghazali, Retna Apsari, M. M. F. Meor Zulkifli, Mohammad Alkhedher

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Unsupervised Performance Evaluation Strategy for Bridge Superstructure Based on Fuzzy Clustering and Field Data by Yubo Jiao, Hanbing Liu, Peng Zhang, Xianqiang Wang, Haibin Wei

“…Finally, different thresholds are selected to form dynamic clustering maps and determine the best classification based on statistic analysis. The clustering result is regarded as a sample base, and the bridge state can be evaluated by calculating the fuzzy nearness between the unknown bridge state data and the sample base. …”
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Local Similarity-Based Fuzzy Multiple Kernel One-Class Support Vector Machine by Qiang He, Qingshuo Zhang, Hengyou Wang, Changlun Zhang

“…In order to solve this problem, the fuzzy membership degree is introduced into OCSVM, which makes the samples with different importance have different influences on the determination of classification hyperplane and enhances the robustness. …”
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DIA-TSK: A Dynamic Incremental Adaptive Takagi–Sugeno–Kang Fuzzy Classifier by Hao Chen, Chenhui Sha, Mingqing Jiao, Changbin Shao, Shang Gao, Hualong Yu, Bin Qin

“…To solve these issues, this study proposes a novel training method consisting of a single dynamic classifier—named the dynamic incremental adaptive Takagi–Sugeno–Kang fuzzy classifier (DIA-TSK)—which leverages the superior non-linear modeling capabilities and interpretability of the TSK fuzzy system. …”
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Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques by Meeniga Venkateswarlu, Srinivas Rallapalli, Amit Singh, G. Sai Sesha Chalapathi, Suresh Kumar, Yashwant Bhaskar Katpatal, Gouligari Sujatha

“…The model integrates 80 fuzzy rules to evaluate macro- and micronutrients, incorporating 250 soil samples analyzed using the PUSA Soil Test and Fertilizer Recommendation Meter (STFR). …”
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Application of Improved MDSMOTE and FC-SVM in Imbalanced Data Set Classification by WEN Xue-yan, ZHAO Li-ying, XU Ke-sheng, LU Guang

“…On the network shopping evaluation data sets appear the phenomenon of extreme imbalance，inorder to improve the classification accuracy of the unbalanced data set，It should be improved from both the sample and the algorithm For one of the problem in MDSMOTE algorithm that when generating part of the new samples， wrong points sample can't be contained，the correct classification of the wrongly classified sample is added to the existing MDSMOTE algorithm to improve the quality of the samples. …”
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Technique for target recognition based on intuitionistic fuzzy c-means clustering and kernel matching pursuit by Yang LEI, Wei-wei KONG, Ying-jie LEI

“…Kernel matching pursuit requires every step of searching process be global optimal searching in the redundant dictionary of function.Namely,the dictionary learning time of KMP was too long.To the above drawbacks,a novel technique for KMP based on IFCM was proposed to substitute local searching for global searching by the property superiority of dynamic clustering performance,which was also the superiority in Intuitionistic fuzzy c-means algorithm.Then two testing including classification and effectiveness were carried out towards four real sample data.Subsequently,high resolution range profile (HRRP)was selected from the classical properties of target recognition in e middle ballistic trajectory,which were extracted for getting sub-range profile.Finally,three algorithms including FCM,KMP,IFCM-KMP were carried out respectively towards different kinds of sub-range profile samples in emulation platform,the conclusion of which fully demonstrates that the IFCM-KMP algorithm is superior over FCM and KMP when it comes to target recognition in the middle ballistic trajectory.…”
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Label credibility correction based on cell morphological differences for cervical cells classification by Wenbo Pang, Yue Qiu, Shu Jin, Huiyan Jiang, Yi Ma

“…To address the problem caused by noisy labels, we propose a method based on label credibility correction for cervical cell images classification network. Firstly, a contrastive learning network is used to extract discriminative features from cell images to obtain more similar intra-class sample features. …”
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Classification with reject option: Distribution-free error guarantees via conformal prediction by Johan Hallberg Szabadváry, Tuwe Löfström, Ulf Johansson, Cecilia Sönströd, Ernst Ahlberg, Lars Carlsson

“…In this work, we formalise the approach to ML with reject option in binary classification, deriving theoretical guarantees on the resulting error rate. …”
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A Method Combining Order Tracking and Fuzzy C-Means for Diesel Engine Fault Detection and Isolation by Ruili Zeng, Lingling Zhang, Yunkui Xiao, Jianmin Mei, Bin Zhou, Huimin Zhao, Jide Jia

“…After standardizing these features, the fuzzy c-means (FCM) is introduced to use them as input vector; the optimized classified matrix and clustering centers can be obtained using FCM iteration method; then the fault can be detected by calculating the approach degree between the unknown samples and the known ones. …”
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A novel ensemble support vector machine model for land cover classification by Ying Liu, Lihua Huang

“…Nowadays, support vector machines are widely applied to land cover classification although this method is sensitive to parameter selection and noise samples. …”
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Wood Species Recognition Based on Visible and Near-Infrared Spectral Analysis Using Fuzzy Reasoning and Decision-Level Fusion by Peng Zhao, Zhen-Yu Li, Cheng-Kun Wang

“…A novel wood species spectral classification scheme is proposed based on a fuzzy rule classifier. …”
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