Publications
Journal Articles: 56, Book Chapter: 07, Conference / Workshop Papers: 24
2024
[NEW] Jain, Sambhav, and Reshma Rastogi., "Parametric non-parallel support vector machines for pattern classification." Machine Learning 113, no. 4 (2024): 1567-1594. DOI: https://doi.org/10.1007/s10994-022-06238-0
2023
Saigal, P., David, A. & Rastogi, R., "Oblique random forests with binary and ternary decision structures and non-parallel hyperplanes classifiers", Int J Data Sci Anal (2023). https://doi.org/10.1007/s41060-023-00472-y
Aatif Nisar Dar, Reshma Rastogi, "MLGAN: Addressing Imbalance in Multilabel Learning Using Generative Adversarial Networks", 2023 Int. Conf. on Emerging Techniques in Computational Intelligence (ICETCI), DOI: 10.1109/ICETCI58599.2023.10331105
S. Jain, R. Rastogi, "Enhancing Pattern Classification in Support Vector Machines through Matrix Formulation", - arXiv preprint arXiv:2307.09372, 2023
Kumar, S., Rastogi, R. (2023). "Auxiliary Label Embedding for Multi-label Learning with Missing Labels". In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_42
Kumar S., Ahmadi N. & Rastogi R., "Multi-label learning with missing labels using sparse global structure for label-specific features", Appl Intell (2023).
Pritam Anand, Amisha Bharti, Reshma Rastogi, "Time efficient variants of Twin Extreme Learning Machine", Intelligent Systems with Applications, Volume 17, 2023, 200169, ISSN 2667-3053., DOI:10.1016/j.iswa.2022.200169
Rastogi, R., Kumar, S., "Discriminatory Label-specific Weights for Multi-label Learning with Missing Labels", Neural Process Lett 55, 1397–1431 (2023). doi: https://doi.org/10.1007/s11063-022-10945-z
2022
Rastogi R., Mortaza S, “Imbalance multi-label data learning with label specific features", Neurocomputing, Volume 513, 2022, pp. 395-408, DOI:10.1016/j.neucom.2022.09.085
Jain S., Rastogi R, “Parametric non-parallel support vector machines for pattern classification", Machine Learning, 2022, DOI:10.1007/s10994-022-06238-0
Rastogi R., Hussain M. (2022), "Robust Multi-task Least Squares Twin Support Vector Machines for Classification. In: Gupta, D., Sambyo, K., Prasad, M., Agarwal, S. (eds) Advanced Machine Intelligence and Signal Processing. Lecture Notes in Electrical Engineering, vol 858. Springer, Singapore.
Kumar S., Rastogi R, “Low rank label subspace transformation for multi-label learning with missing labels", Information Sciences, Volume 596, 2022, pp. 53-72, DOI:10.1016/j.ins.2022.03.015
R Rastogi, S Jain, "Multi-label learning via Minimax Probability Machine", International Journal of Approximate Reasoning, Volume 145, 2022, Pages 1-17.
S. Jain, S. S. Roy and R. Rastogi, "Neo-Twin Support Vector Machines for Pattern Classification," 2022 International Conference on Decision Aid Sciences and Applications (DASA), Chiangrai, Thailand, 2022, pp. 347-351, doi: 10.1109/DASA54658.2022.9765240.
Tanveer, M., Rajani, T., Rastogi, R. et al., "Comprehensive review on twin support vector machines. Ann Oper Res (2022)",
DOI: https://doi.org/10.1007/s10479-022-04575-wJain, S., & Rastogi, R. (2021), "Multi-label Minimax Probability Machine with Multi-manifold Regularisation", Research Reports on Computer Science, 1(1), 44–63.
2021
Rastogi, R. and Mortaza, S., “Multi-label classification with Missing Labels using Label Correlation and Robust Structural Learning”, Knowledge Based Systems, 2021, 229, 107336.
Mohammad Tanveer, Reshma Rastogi, Chin-Teng Lin, "Advanced machine learning algorithms for biomedical data and imaging", Multimed Tools Appl 80, 30005 (2021). DOI: https://doi.org/10.1007/s11042-021-11330-z
R Rastogi, A Pal, "Efficient Learning of Pinball TWSVM using Privileged Information and its applications", arXiv preprint arXiv:2107.06744
P Anand, R Rastogi, S Chandra, "Improvement over Pinball Loss Support Vector Machine", arXiv preprint arXiv:2106.01109
Rastogi, R., Sharma, S., "Ternary tree-based structural twin support tensor machine for clustering", Pattern Analysis and Applications, Springer, 2021, 24 (1), 61-74, doi: https://doi.org/10.1007/s10044-020-00902-8
M Tanveer, S Sharma, R Rastogi, P Anand, “Sparse support vector machine with pinball loss”, Transactions on Emerging Telecommunications Technologies 32 (2), e3820, 2021, 1-6., doi: https://doi.org/10.1002/ett.3820
R Rastogi, S Jain, "Multi-label Minimax Probability Machine with Multi-manifold Regularisation", Research Reports on Computer Science (2021), Volume 1 Issue 1|2021| 45.
2020
P Anand, R Rastogi, S Chandra, ``A class of new support vector regression models”, Applied Soft Computing, 2020, 94, 106446
P Anand, R Rastogi, S Chandra,“A new asymmetric ϵ-insensitive pinball loss function based support vector quantile regression model”, Applied Soft Computing, 2020, 94, 1064-73
R Rastogi, P Anand, S Chandra, “Large-margin distribution machine-based regression”, Neural Computing and Applications Springer,2020, 32 (8), 3633-3648
P Saigal, R Rastogi, S Chandra, “Semi-supervised Weighted Ternary Decision Structure for Multi-category Classification”, Neural Processing Letters, Springer, 2020, 52 (2), 1555-1582
2019
Sharma, S., Rastogi, R., & Chandra, S. (2019). Large-Scale Twin Parametric Support Vector Machine Using Pinball Loss Function. IEEE Transactions on Systems, Man, and Cybernetics: Systems. (IF=5.131)
Rastogi, R., & Pal, A. (2019). Fuzzy semi-supervised weighted linear loss twin support vector clustering. Knowledge-Based Systems, 165, 132-148. (IF=5.11)
Rastogi, R., & Sharma, S. (2019). Fast Laplacian twin support vector machine with active learning for pattern classification. Applied Soft Computing, 74, 424-439. (IF=4.81)
Kumar, A., & Rastogi, R. (2019). Attentional Recurrent Neural Networks for Sentence Classification. In Innovations in Infrastructure (pp. 549-559). Springer, Singapore.
Rastogi, R., & Bharti, A. (2019). Least Squares Twin Extreme Learning Machine for Pattern Classification. In Innovations in Infrastructure (pp. 561-571). Springer, Singapore.
Anand, P., Rastogi, R., & Chandra, S. (2019). Generalized $$\varepsilon $$—Loss Function-Based Regression. In Machine Intelligence and Signal Analysis (pp. 395-409). Springer, Singapore.
Anand, P., Pandey, J. P., Rastogi, R., & Chandra, S. (2019). A Privacy-Preserving Twin Support Vector Machine Classifier for Vertical Partitioned Data. In Computational Intelligence: Theories, Applications and Future Directions-Volume I (pp. 539-552). Springer, Singapore.
2018
Sharma, S., & Rastogi, R. (2018, December). Stochastic Conjugate Gradient Descent Twin Support Vector Machine for Large Scale Pattern Classification. In Australasian Joint Conference on Artificial Intelligence (pp. 590-602). Springer, Cham.
Pal, A. & Rastogi, R., " Learning TWSVM using Privilege Information," Accepted at IEEE- Symposium Series on Computational Intelligence, Held on 18-21 Nov. 2018, India.
Anand, P. , Rastogi, R. & Bharti, A., " A Pinball loss based Extreme Learning Machine for pattern classification," Accepted at IEEE- Symposium Series on Computational Intelligence, Held on 18-21 Nov. 2018, India.
Kumar, A. & Rastogi, R., " Self-Attention Enhanced Recurrent Neural Networks for Sentence Classification," Accepted at IEEE- Symposium Series on Computational Intelligence, Held on 18-21 Nov. 2018, India.
Rastogi, R., Safdari, H. & and Sharma, S., "Insensitive Zone based Pinball Loss Twin Support Vector Machine for Pattern Classification," Accepted at IEEE- Symposium Series on Computational Intelligence, Held on 18-21 Nov. 2018, India.
Sharma, S., & Rastogi, R., "Insensitive Zone based Pinball Loss Twin Support Vector Machine for Pattern Classification," Accepted at IEEE-Symposium Series on Computational Intelligence, Held on 18-21 Nov. 2018, India.
Sharma, S., & Rastogi, R., "Maximum Margin Minimum Variance Twin Support Vector Machine for Pattern Classification," Accepted at IEEE-Symposium Series on Computational Intelligence, Held on 18-21 Nov. 2018, India.
Rastogi, R., Pal, A., & Chandra, S. (2018). Generalized Pinball Loss SVMs. Neurocomputing, 322, 161-165.
Khemchandani, R., Goyal, K., & Chandra, S. (2018). Generalized eigenvalue proximal support vector regressor for the simultaneous learning of a function and its derivatives. International Journal of Machine Learning and Cybernetics, 9(12), 2059-2070.
Rastogi, R., & Pal, A. (2018). Fuzzy semi-supervised weighted linear loss twin support vector clustering. Knowledge-Based Systems. (IF=5.11)
Khemchandani, R., Saigal, P., & Chandra, S. (2018). Angle-based twin support vector machine. Annals of Operations Research, 269(1-2), 387-417. (IF=1.864)
Rastogi, R., Sharma, S., & Chandra, S. (2018). Robust parametric twin support vector machine for pattern classification. Neural Processing Letters, 47(1), 293-323. (IF=1.787)
Rastogi, R., Anand, P., & Chandra, S. (2018). Large-margin Distribution Machine-based regression. Neural Computing and Applications, 1-16. (IF=4.213)
Rastogi, R., Saigal, P., & Chandra, S. (2018). Angle-based twin parametric-margin support vector machine for pattern classification. Knowledge-Based Systems, 139, 64-77. (IF=5.11)
Khemchandani, R., Pal, A., & Chandra, S. (2018). Fuzzy least squares twin support vector clustering. Neural computing and applications, 29(2), 553-563. (IF=4.213)
2017
Rastogi, R., & Sharma, S. (2017, December). Tree-Based Structural Twin Support Tensor Clustering with Square Loss Function. In International Conference on Pattern Recognition and Machine Intelligence (pp. 28-34). Springer, Cham.
Rastogi, R., Anand, P., & Chandra, S. (2017). L1-norm Twin Support Vector Machine-based Regression. Optimization, 66(11), 1895-1911.
Khemchandani, R., & Pal, A. (2017). Tree based multi-category Laplacian TWSVM for content based image retrieval. International Journal of Machine Learning and Cybernetics, 8(4), 1197-1210. (IF=2.692)
Rastogi, R., & Saigal, P. (2017). Tree-based localized fuzzy twin support vector clustering with square loss function. Applied Intelligence, 47(1), 96-113. (IF=1.983)
Saigal, P., Khanna, V., & Rastogi, R. (2017). Divide and conquer approach for semi-supervised multi-category classification through localized kernel spectral clustering. Neurocomputing, 238, 296-306. (IF=3.74)
Rastogi, R., Anand, P., & Chandra, S. (2017). A ν-twin support vector machine based regression with automatic accuracy control. Applied Intelligence, 46(3), 670-683. (IF=1.983)
Khemchandani, R., & Sharma, S. (2017). Robust Parametric Twin Support Vector Machine and Its Application in Human Activity Recognition. In Proceedings of International Conference on Computer Vision and Image Processing (pp. 193-203). Springer, Singapore.
Khemchandani, R., & Chandra, S. (2017). TWSVR: Twin Support Vector Machine Based Regression. In Twin Support Vector Machines (pp. 63-101). Springer, Cham.
2016
Khemchandani, R., & Sharma, S. (2016). Robust least squares twin support vector machine for human activity recognition. Applied Soft Computing, 47, 33-46. (IF=4.81)
Khemchandani, R., & Pal, A. (2016). Multi-category laplacian least squares twin support vector machine. Applied Intelligence, 45(2), 458-474. (IF=1.983)
Khemchandani, R., Bhardwaj, A., & Chandra, S. (2016). Single asset optimal trading strategies with stochastic dominance constraints. Annals of Operations Research, 243(1-2), 211-228. (IF=1.864)
Khemchandani, R., Saigal, P., & Chandra, S. (2016). Improvements on ν-twin support vector machine. Neural Networks, 79, 97-107. (IF=7.56)
Khemchandani, R., & Pal, A. (2016, March). Weighted Linear Loss Twin Support Vector Clustering. In Proceedings of the 3rd IKDD Conference on Data Science, 2016 (p. 18). ACM.
Saigal, P., & Khemchandani, R. (2016, December). Nonparallel hyperplane classifiers for multi-category classification. In Computational Intelligence: Theories, Applications and Future Directions (WCI), 2016 IEEE Workshop on (pp. 1-6). IEEE.
2015
Reshma Khemchandani, Pooja Saigal: Color image classification and retrieval through ternary decision structure based multi-category TWSVM. Neurocomputing 165: 444-455 (2015(IF=3.74)
Reshma Khemchandani, Keshav Goyal, Suresh Chandra: Twin Support Vector Machine based Regression. ICAPR 2015: 1-6 (IF=7.56)
2014
Khemchandani R., Chandra S.. (2014). Efficient trading frontier: a shortage function approach. Applied Soft Computing, 63, 1533-1548. DOI:10.1080/02331934.2014.883508 .
2013
Reshma Khemchandani, Anuj Karpatne, Suresh Chandra: Twin support vector regression for the simultaneous learning of a function and its derivatives. Int. J. Mach. Learn. Cybern. 4(1): 51-63 (2013)
Reshma Khemchandani, Anuj Karpatne, Suresh Chandra: Proximal support tensor machines. Int. J. Mach. Learn. Cybern. 4(6): 703-712 (2013)
Reshma Khemchandani, Nishil Gupta, Arpit Chaudhary, Suresh Chandra: Optimal execution with weighted impact functions: a quadratic programming approach. Optim. Lett. 7(3): 575-592 (2013)
2011
Reshma Khemchandani, Anuj Karpatne, Suresh Chandra: Generalized eigenvalue proximal support vector regressor. Expert Syst. Appl. 38(10): 13136-13142 (2011)
2010
Reshma Khemchandani, Jayadeva, Suresh Chandra: Learning the optimal kernel for Fisher discriminant analysis via second order cone programming. Eur. J. Oper. Res. 203(3): 692-697 (2010)
M. Arun Kumar, Reshma Khemchandani, Madan Gopal, Suresh Chandra: Knowledge based Least Squares Twin support vector machines. Inf. Sci. 180(23): 4606-4618 (2010)
2009
Reshma Khemchandani, Jayadeva, Suresh Chandra: Knowledge based proximal support vector machines. Eur. J. Oper. Res. 195(3): 914-923 (2009)
Reshma Khemchandani, Jayadeva, Suresh Chandra: Regularized least squares fuzzy support vector regression for financial time series forecasting. Expert Syst. Appl. 36(1): 132-138 (2009)
Reshma Khemchandani, Jayadeva, Suresh Chandra: Optimal kernel selection in twin support vector machines. Optim. Lett. 3(1): 77-88 (2009)
2008
Khemchandani R., Jayadeva, Chandra S. (2008), Linear potential proximal support vector machines for pattern classification, Optimization Methods and Software, 23, 491-500. DOI:10.1080/10556780802102636.
Jayadeva, Khemchandani R., Chandra S.. (2008), Regularized least squares support vector regression for the simultaneous learning of a function and its derivatives, Information Sciences, 178, 3402-3414. DOI:10.1016/j.ins.2008.04.007.
2007
Jayadeva A., Khemchandani R., Chandra S. (2007), Fuzzy multi-category proximal support vector classification via generalized eigenvalues, Soft Computing, 11, 679-685. DOI:10.1007/s00500-006-0130-2 .
Jayadeva A., Khemchandani R., Chandra S. (2007), Twin support vector machines for pattern classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 905-910. DOI:10.1109/TPAMI.2007.1068 .
2006
Jayadeva, Reshma Khemchandani, Suresh Chandra: Regularized Least Squares Fuzzy Support Vector Regression for Time Series Forecasting. IJCNN 2006: 593-598
Jayadeva, Reshma Khemchandani, Suresh Chandra: Regularized Least Squares Twin SVR for the Simultaneous Learning of a Function and its Derivative. IJCNN 2006: 1192-1197
2005
Jayadeva A., Khemchandani R., Chandra S. (2005): Fuzzy linear proximal support vector machines for multi-category data classification, Neurocomputing, 67, 426-435. DOI:10.1016/j.neucom.2004.09.002.
Jayadeva, Reshma Khemchandani, Suresh Chandra: Fuzzy Proximal Support Vector Classification Via Generalized Eigenvalues. PReMI 2005: 360-363
2004
Jayadeva A., Khemchandani R., Chandra S.. (2004). Fast and robust learning through fuzzy linear proximal support vector machines, Neurocomputing, 61, 401-411. DOI:10.1016/j.neucom.2004.02.004 .