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Journal articles on the topic 'Robust Classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 June 2025

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1

Bertsimas, Dimitris, Jack Dunn, Colin Pawlowski, and Ying Daisy Zhuo. "Robust Classification." INFORMS Journal on Optimization 1, no. 1 (2019): 2–34. http://dx.doi.org/10.1287/ijoo.2018.0001.

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2

Chen, Li, Cencheng Shen, Joshua T. Vogelstein, and Carey E. Priebe. "Robust Vertex Classification." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 3 (2016): 578–90. http://dx.doi.org/10.1109/tpami.2015.2456913.

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3

Glendinning, R. H. "Robust shape classification." Signal Processing 77, no. 2 (1999): 121–38. http://dx.doi.org/10.1016/s0165-1684(99)00028-6.

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4

Addison, W. D., and R. H. Glendinning. "Robust image classification." Signal Processing 86, no. 7 (2006): 1488–501. http://dx.doi.org/10.1016/j.sigpro.2005.08.010.

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5

Zhang, Jun, Xiao Chen, Yang Xiang, Wanlei Zhou, and Jie Wu. "Robust Network Traffic Classification." IEEE/ACM Transactions on Networking 23, no. 4 (2015): 1257–70. http://dx.doi.org/10.1109/tnet.2014.2320577.

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6

Katz, Alan J., Michael T. Gately, and Dean R. Collins. "Robust Classifiers without Robust Features." Neural Computation 2, no. 4 (1990): 472–79. http://dx.doi.org/10.1162/neco.1990.2.4.472.

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We develop a two-stage, modular neural network classifier and apply it to an automatic target recognition problem. The data are features extracted from infrared and TV images. We discuss the problem of robust classification in terms of a family of decision surfaces, the members of which are functions of a set of global variables. The global variables characterize how the feature space changes from one image to the next. We obtain rapid training times and robust classification with this modular neural network approach.

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7

Lai, Yu-kun, Qian-yi Zhou, Shi-min Hu, Johannes Wallner, and Helmut Pottmann. "Robust Feature Classification and Editing." IEEE Transactions on Visualization and Computer Graphics 13, no. 1 (2007): 34–45. http://dx.doi.org/10.1109/tvcg.2007.19.

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8

Hubert, Mia, and Stephan Van der Veeken. "Robust classification for skewed data." Advances in Data Analysis and Classification 4, no. 4 (2010): 239–54. http://dx.doi.org/10.1007/s11634-010-0066-3.

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9

Kundur, D., D. Hatzinakos, and H. Leung. "Robust classification of blurred imagery." IEEE Transactions on Image Processing 9, no. 2 (2000): 243–55. http://dx.doi.org/10.1109/83.821737.

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10

Zhang, Lingling, Minnan Luo, Zhihui Li, et al. "Large-Scale Robust Semisupervised Classification." IEEE Transactions on Cybernetics 49, no. 3 (2019): 907–17. http://dx.doi.org/10.1109/tcyb.2018.2789420.

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Lecué, Guillaume, Matthieu Lerasle, and Timlothée Mathieu. "Robust classification via MOM minimization." Machine Learning 109, no. 8 (2020): 1635–65. http://dx.doi.org/10.1007/s10994-019-05863-6.

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12

Balakrishnan, N., M. L. Tiku, and A. H. El Shaarawi. "Robust Univariate Two-Way Classification." Biometrical Journal 27, no. 2 (1985): 123–38. http://dx.doi.org/10.1002/bimj.4710270202.

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13

Rezaei, Ashkan, Rizal Fathony, Omid Memarrast, and Brian Ziebart. "Fairness for Robust Log Loss Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5511–18. http://dx.doi.org/10.1609/aaai.v34i04.6002.

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Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications. Many existing methods enforce fairness constraints on a selected classifier (e.g., logistic regression) by directly forming constrained optimizations. We instead re-derive a new classifier from the first principles of distributional robustness that incorporates fairness criteria into a worst-case logarithmic loss minimization. This construction takes the form of a minimax game and produces a parametri

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14

Landeiro, Virgile, and Aron Culotta. "Robust Text Classification under Confounding Shift." Journal of Artificial Intelligence Research 63 (November 5, 2018): 391–419. http://dx.doi.org/10.1613/jair.1.11248.

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   As statistical classifiers become integrated into real-world applications, it is important to consider not only their accuracy but also their robustness to changes in the data distribution. Although identifying and controlling for confounding variables Z - correlated with both the input X of a classifier and its output Y - has been assiduously studied in empirical social science, it is often neglected in text classification. This can be understood by the fact that, if we assume that the impact of confounding variables does not change between the time we fit a model and the

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15

Asimit, Alexandru V., Ioannis Kyriakou, Simone Santoni, Salvatore Scognamiglio, and Rui Zhu. "Robust Classification via Support Vector Machines." Risks 10, no. 8 (2022): 154. http://dx.doi.org/10.3390/risks10080154.

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Classification models are very sensitive to data uncertainty, and finding robust classifiers that are less sensitive to data uncertainty has raised great interest in the machine learning literature. This paper aims to construct robust support vector machine classifiers under feature data uncertainty via two probabilistic arguments. The first classifier, Single Perturbation, reduces the local effect of data uncertainty with respect to one given feature and acts as a local test that could confirm or refute the presence of significant data uncertainty for that particular feature. The second class

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16

Pelofske, Elijah, Lorie M. Liebrock, and Vincent Urias. "A Robust Cybersecurity Topic Classification Tool." International Journal of Network Security & Its Applications 14, no. 1 (2022): 1–25. http://dx.doi.org/10.5121/ijnsa.2022.14101.

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In this research, we use user defined labels from three internet text sources (Reddit, StackExchange, Arxiv) to train 21 different machine learning models for the topic classification task of detecting cybersecurity discussions in natural English text. We analyze the false positive and false negative rates of each of the 21 model’s in cross validation experiments. Then we present a Cybersecurity Topic Classification (CTC) tool, which takes the majority vote of the 21 trained machine learning models as the decision mechanism for detecting cybersecurity related text. We also show that the majori

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17

Ptucha, Raymond, and Andreas E. Savakis. "LGE-KSVD: Robust Sparse Representation Classification." IEEE Transactions on Image Processing 23, no. 4 (2014): 1737–50. http://dx.doi.org/10.1109/tip.2014.2303648.

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18

Clancy, T. Charles, Awais Khawar, and Timothy R. Newman. "Robust Signal Classification Using Unsupervised Learning." IEEE Transactions on Wireless Communications 10, no. 4 (2011): 1289–99. http://dx.doi.org/10.1109/twc.2011.030311.101137.

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19

Li, Bo, and Yevgeniy Vorobeychik. "Evasion-Robust Classification on Binary Domains." ACM Transactions on Knowledge Discovery from Data 12, no. 4 (2018): 1–32. http://dx.doi.org/10.1145/3186282.

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20

Zhang, Xunan, Shiji Song, and Cheng Wu. "Robust Bayesian Classification with Incomplete Data." Cognitive Computation 5, no. 2 (2012): 170–87. http://dx.doi.org/10.1007/s12559-012-9188-6.

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21

Alaíz, Carlos M., Michaël Fanuel, and Johan A. K. Suykens. "Robust classification of graph-based data." Data Mining and Knowledge Discovery 33, no. 1 (2018): 230–51. http://dx.doi.org/10.1007/s10618-018-0603-9.

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22

Schwager, Mac, Dean M. Anderson, Zack Butler, and Daniela Rus. "Robust classification of animal tracking data." Computers and Electronics in Agriculture 56, no. 1 (2007): 46–59. http://dx.doi.org/10.1016/j.compag.2007.01.002.

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23

Liu, Yang, Chenyu Liu, Yufang Tang, Haixu Liu, Shuxin Ouyang, and Xueming Li. "Robust block sparse discriminative classification framework." Journal of the Optical Society of America A 31, no. 12 (2014): 2806. http://dx.doi.org/10.1364/josaa.31.002806.

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24

Maheswari, A. Uma, and K. Latha. "Unsupervised Learning for Robust Signal Classification." Applied Mechanics and Materials 573 (June 2014): 429–34. http://dx.doi.org/10.4028/www.scientific.net/amm.573.429.

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Cognitive Radio Networks (CRNs) have been proposed to increase the efficiency of channel utilization; presently the demand for wireless bandwidth is increased. Cognitive Radio Network can enable the sharing of channels among unlicensed and licensed users on a non-interference basis. An unlicensed user (i.e., secondary user) should monitor for the presence of a licensed user (i.e., primary user) to avoid interfering with a primary user. However to get more gain, an attacker also called self-ish secondary user may copy a primary user’s signal to cheat other secondary users. Therefore a primary u

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25

Hubert, M., and S. Engelen. "Robust PCA and classification in biosciences." Bioinformatics 20, no. 11 (2004): 1728–36. http://dx.doi.org/10.1093/bioinformatics/bth158.

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26

Bosdogianni, P., M. Petrou, and J. Kittler. "Mixed pixel classification with robust statistics." IEEE Transactions on Geoscience and Remote Sensing 35, no. 3 (1997): 551–59. http://dx.doi.org/10.1109/36.581966.

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27

Dutta, Subhajit, and Anil K. Ghosh. "On robust classification using projection depth." Annals of the Institute of Statistical Mathematics 64, no. 3 (2011): 657–76. http://dx.doi.org/10.1007/s10463-011-0324-y.

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28

Qian, Jianjun, Shumin Zhu, Wai Keung Wong, Hengmin Zhang, Zhihui Lai, and Jian Yang. "Dual robust regression for pattern classification." Information Sciences 546 (February 2021): 1014–29. http://dx.doi.org/10.1016/j.ins.2020.09.062.

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29

Balcan, Maria-Florina, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, and Gregory B. Sorkin. "Robust reductions from ranking to classification." Machine Learning 72, no. 1-2 (2008): 139–53. http://dx.doi.org/10.1007/s10994-008-5058-6.

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30

Elijah, Pelofske, M. Liebrock Lorie, and Urias Vincent. "A Robust Cybersecurity Topic Classification Tool." International Journal of Network Security & Its Applications (IJNSA) 14, no. 1 (2022): 01–25. https://doi.org/10.5281/zenodo.6017328.

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In this research, we use user defined labels from three internet text sources (Reddit, StackExchange, Arxiv) to train 21 different machine learning models for the topic classification task of detecting cybersecurity discussions in natural English text. We analyze the false positive and false negative rates of each of the 21 model’s in cross validation experiments. Then we present a Cybersecurity Topic Classification (CTC) tool, which takes the majority vote of the 21 trained machine learning models as the decision mechanism for detecting cybersecurity related text. We also show that the

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31

Zou, Cuiming, Yuan Yan Tang, Yulong Wang, and Zhenghua Luo. "Huber collaborative representation for robust multiclass classification." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 04 (2019): 1950020. http://dx.doi.org/10.1142/s0219691319500206.

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Recent advances have shown a great potential of collaborative representation (CR) for multiclass classification. However, conventional CR-based classification methods adopt the mean square error (MSE) criterion as the cost function, which is sensitive to gross corruption and outliers. To address this limitation, inspired by the success of robust statistics, we develop a Huber collaborative representation-based classification (HCRC) method for robust multiclass classification. Concretely, we cast the classification problem as a Huber collaborative representation problem with the Huber estimator

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32

Obayya, Marwa, Mohammad Alamgeer, Jaber S. Alzahrani, et al. "Artificial Intelligence Driven Biomedical Image Classification for Robust Rheumatoid Arthritis Classification." Biomedicines 10, no. 11 (2022): 2714. http://dx.doi.org/10.3390/biomedicines10112714.

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Recently, artificial intelligence (AI) including machine learning (ML) and deep learning (DL) models has been commonly employed for the automated disease diagnosis process. AI in biological and biomedical imaging is an emerging area and will be a future trend in the field. At the same time, biomedical images can be used for the classification of Rheumatoid arthritis (RA) diseases. RA is an autoimmune illness that affects the musculoskeletal system causing systemic, inflammatory and chronic effects. The disease frequently becomes progressive and decreases physical function, causing articular da

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33

Zhao, Lei. "A Robust Loss Function for Multiclass Classification." International Journal of Machine Learning and Computing 3, no. 6 (2013): 462–67. http://dx.doi.org/10.7763/ijmlc.2013.v3.361.

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34

Vu Dang, Nguyen Trinh, Loc Tran, and Linh Tran. "Noise-robust classification with hypergraph neural network." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 3 (2021): 1465. http://dx.doi.org/10.11591/ijeecs.v21.i3.pp1465-1473.

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<p>This paper presents a novel version of hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional reduction technique to the feature matrices of the image datasets in order to reduce the “noise” and the redundant features in the feature matrices of the image datasets and to reduce the runtime constructing the hypergraph of the hypergraph neural network method. Then, the classic graph based semisupervised learning method, the classic hypergraph based semi-supervised learning method, the graph neural network,

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35

Mansor, Muhammad Naufal, and Mohd Nazri Rejab. "A Robust Neonatal Facial Pain Cues Classification." Applied Mechanics and Materials 475-476 (December 2013): 1110–17. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.1110.

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Late of infant pain detection on the early stage may affect newborns growth. Regarding of this matter, different techniques have been proposed such as facial expressions, speech production variation, and physiological signals to detect the pain states of a person. For past 2 decades, the determination of pain state through images has been undergone substantial research and development. Various techniques are used in the literature to classify pain states on the basis of images. In this paper, a feature extraction method using Principal Component Analysis (PCA) was adopted for identifying the p

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36

Petety, Aditya, Sandhya Tripathi, and N. Hemachandra. "Attribute Noise Robust Binary Classification (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13897–98. http://dx.doi.org/10.1609/aaai.v34i10.7221.

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We consider the problem of learning linear classifiers when both features and labels are binary. In addition, the features are noisy, i.e., they could be flipped with an unknown probability. In Sy-De attribute noise model, where all features could be noisy together with same probability, we show that 0-1 loss (l0−1) need not be robust but a popular surrogate, squared loss (lsq) is. In Asy-In attribute noise model, we prove that l0−1 is robust for any distribution over 2 dimensional feature space. However, due to computational intractability of l0−1, we resort to lsq and observe that it need no

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37

Nanopoulos, Alexandros, Apostolos N. Papadopoulos, Yannis Manolopoulos, and Tatjana Welzer-Druzovec. "Robust Classification Based on Correlations Between Attributes." International Journal of Data Warehousing and Mining 3, no. 3 (2007): 14–27. http://dx.doi.org/10.4018/jdwm.2007070102.

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38

Suzuki, Ikumi, Takashi Takenouchi, Miki Ohira, Shigeyuki Oba, and Shin Ishii. "Robust Model Selection for Classification of Microarrays." Cancer Informatics 7 (January 2009): CIN.S2704. http://dx.doi.org/10.4137/cin.s2704.

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39

Wang, Shuhua, and Baohuai Sheng. "Learning Rates of Kernel-Based Robust Classification." Acta Mathematica Scientia 42, no. 3 (2022): 1173–90. http://dx.doi.org/10.1007/s10473-022-0321-7.

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40

Vos, Daniël, and Sicco Verwer. "Robust Optimal Classification Trees against Adversarial Examples." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8520–28. http://dx.doi.org/10.1609/aaai.v36i8.20829.

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Decision trees are a popular choice of explainable model, but just like neural networks, they suffer from adversarial examples. Existing algorithms for fitting decision trees robust against adversarial examples are greedy heuristics and lack approximation guarantees. In this paper we propose ROCT, a collection of methods to train decision trees that are optimally robust against user-specified attack models. We show that the min-max optimization problem that arises in adversarial learning can be solved using a single minimization formulation for decision trees with 0-1 loss. We propose such for

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41

Mukhaimar, Ayman, Ruwan Tennakoon, Chow Yin Lai, Reza Hoseinnezhad, and Alireza Bab-Hadiashar. "Robust Object Classification Approach Using Spherical Harmonics." IEEE Access 10 (2022): 21541–53. http://dx.doi.org/10.1109/access.2022.3151350.

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42

Blanco, Victor, Alberto Japón, and Justo Puerto. "Robust optimal classification trees under noisy labels." Advances in Data Analysis and Classification 16, no. 1 (2021): 155–79. http://dx.doi.org/10.1007/s11634-021-00467-2.

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AbstractIn this paper we propose a novel methodology to construct Optimal Classification Trees that takes into account that noisy labels may occur in the training sample. The motivation of this new methodology is based on the superaditive effect of combining together margin based classifiers and outlier detection techniques. Our approach rests on two main elements: (1) the splitting rules for the classification trees are designed to maximize the separation margin between classes applying the paradigm of SVM; and (2) some of the labels of the training sample are allowed to be changed during the

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43

Blanco, Victor, Alberto Japón, and Justo Puerto. "Robust optimal classification trees under noisy labels." Advances in Data Analysis and Classification 16, no. 1 (2021): 155–79. http://dx.doi.org/10.1007/s11634-021-00467-2.

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AbstractIn this paper we propose a novel methodology to construct Optimal Classification Trees that takes into account that noisy labels may occur in the training sample. The motivation of this new methodology is based on the superaditive effect of combining together margin based classifiers and outlier detection techniques. Our approach rests on two main elements: (1) the splitting rules for the classification trees are designed to maximize the separation margin between classes applying the paradigm of SVM; and (2) some of the labels of the training sample are allowed to be changed during the

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44

Ramashini, Murugaiya, P. Emeroylariffion Abas, Kusuma Mohanchandra, and Liyanage C. De Silva. "Robust cepstral feature for bird sound classification." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1477. http://dx.doi.org/10.11591/ijece.v12i2.pp1477-1487.

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Birds are excellent environmental indicators and may indicate sustainability of the ecosystem; birds may be used to provide provisioning, regulating, and supporting services. Therefore, birdlife conservation-related researches always receive centre stage. Due to the airborne nature of birds and the dense nature of the tropical forest, bird identifications through audio may be a better solution than visual identification. The goal of this study is to find the most appropriate cepstral features that can be used to classify bird sounds more accurately. Fifteen (15) endemic Bornean bird sounds hav

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45

Zhao, Lei, Musa Mammadov, and John Yearwood. "A new loss function for robust classification." Intelligent Data Analysis 18, no. 4 (2014): 697–715. http://dx.doi.org/10.3233/ida-140664.

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46

Mianji, Fereidoun A., and Ye Zhang. "Robust Hyperspectral Classification Using Relevance Vector Machine." IEEE Transactions on Geoscience and Remote Sensing 49, no. 6 (2011): 2100–2112. http://dx.doi.org/10.1109/tgrs.2010.2103381.

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47

Monasterio, Violeta, Fred Burgess, and Gari D. Clifford. "Robust classification of neonatal apnoea-related desaturations." Physiological Measurement 33, no. 9 (2012): 1503–16. http://dx.doi.org/10.1088/0967-3334/33/9/1503.

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48

Patras, Ioannis, and E. R. Hancock. "Coupled Prediction Classification for Robust Visual Tracking." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 9 (2010): 1553–67. http://dx.doi.org/10.1109/tpami.2009.175.

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49

Ding, Zhengming, and Yun Fu. "Robust Transfer Metric Learning for Image Classification." IEEE Transactions on Image Processing 26, no. 2 (2017): 660–70. http://dx.doi.org/10.1109/tip.2016.2631887.

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50

Li, Chang, Yong Ma, Xiaoguang Mei, Chengyin Liu, and Jiayi Ma. "Hyperspectral Image Classification With Robust Sparse Representation." IEEE Geoscience and Remote Sensing Letters 13, no. 5 (2016): 641–45. http://dx.doi.org/10.1109/lgrs.2016.2532380.

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