Grau, I., Nápoles, G., Sengupta, D., García Lorenzo, M.M. and Nowe, A. 2017. Training set edition using Rough Set Theory for Semi-supervised Classification. 2nd International Symposium on Fuzzy and Rough Sets. Villa Clara, Cuba 24 - 26 Oct 2017 Editorial Feijoó.
| Title | Training set edition using Rough Set Theory for Semi-supervised Classification |
|---|---|
| Authors | Grau, I., Nápoles, G., Sengupta, D., García Lorenzo, M.M. and Nowe, A. |
| Type | Conference paper |
| Abstract | Semi-supervised Classification (SSC) is becoming an attractive research filed due to the emergence of real-world problems on which the number of unlabeled examples exceeds the labeled ones. The natural complexity of this kind of problems rices up when designing algorithms with some interpretability features. In order to overcome this challenge, a novel SSC model called Self-labeling Grey-box (SlGb) has been recently proposed. The SlGb algorithm uses a black-box classifier to enlarge the dataset with the unlabeled examples and a white-box to build an interpretable model. In this paper, we attempt boosting the prediction rates of the SlGb algorithm by editing the training set using the knowledge acquired with rough sets. This can be achieved by weighting the instances according to their inclusion degree to rough information granules before building the final, white-box classification model. |
| Year | 2017 |
| Conference | 2nd International Symposium on Fuzzy and Rough Sets |
| Publisher | Editorial Feijoó |
| Publication dates | |
| Published | Oct 2017 |
Doctors’ and nurses’ eating practices during shift work: Findings from a qualitative study
Sum, K., Cheshire, A., Ridge, Damien T., Sengupta, D. and Deb, D.S. 2024. Doctors’ and nurses’ eating practices during shift work: Findings from a qualitative study. Proceedings of the Nutrition Society. 83 (OCE2), p. E204. https://doi.org/10.1017/s0029665124004282
The Cavendish Living lab - a multidisciplinary, vertically integrated project focused on sustainability
Basnett, P., Percy, L., Sengupta, D. and Smith, C.L. 2023. The Cavendish Living lab - a multidisciplinary, vertically integrated project focused on sustainability. Westminster Learning and Teaching Symposium 2023: Better Than the Real Thing? Exploring Education Futures at the University of Westminster. University of Westminster 04 Sep 2023
Antiviral Drug Target Identification and Ligand Discovery
Patel, Hershna and Sengupta, Dipankar 2023. Antiviral Drug Target Identification and Ligand Discovery. in: Gore, M. and Jagtap, U.B. (ed.) Computational Drug Discovery and Design Springer.
Smart Urban Metabolism: A Big-Data and Machine Learning Perspective
Ruchira Ghosh and Dipankar Sengupta 2023. Smart Urban Metabolism: A Big-Data and Machine Learning Perspective. in: Urban Metabolism and Climate Change Springer. pp. 325–344
Inhibiting CDK4/6 in pancreatic ductal adenocarcinoma via microRNA-21
Mortoglou, M., Miralles, F., Mould, R., Sengupta, D. and Uysal Onganer, P. 2023. Inhibiting CDK4/6 in pancreatic ductal adenocarcinoma via microRNA-21. European Journal of Cell Biology. 102 (2) 151318. https://doi.org/10.1016/j.ejcb.2023.151318
Interpretable semisupervised classifier for predicting cancer stages
Grau, I., Sengupta, D. and Nowe, A. 2021. Interpretable semisupervised classifier for predicting cancer stages. in: Kumar, P., Kumar, Y. and Tawhid, M.A. (ed.) Machine Learning, Big Data, and IoT for Medical Informatics Academic Press. pp. 241-259
Artificial Intelligence in Precision Medicine: A Perspective in Biomarker and Drug Discovery
Sengupta, D. and Santoshi, S. 2021. Artificial Intelligence in Precision Medicine: A Perspective in Biomarker and Drug Discovery. in: Saxena, A. and Chandra, S. (ed.) Artificial Intelligence and Machine Learning in Healthcare Springer. pp. 71-88
An ensemble approach for evaluating the cognitive performance of human population at high altitude
Sengupta, D., Sharma, V.K., Hota, S.K., Srivastava, R.B. and Naik, P.K. 2021. An ensemble approach for evaluating the cognitive performance of human population at high altitude. in: Kumar, P., Kumar, Y. and Tawhid, M.A. (ed.) Machine Learning, Big Data, and IoT for Medical Informatics Academic Press. pp. 165-178
Machine learning in precision medicine
Sengupta, D. 2021. Machine learning in precision medicine. in: Kumar, P., Kumar, Y. and Tawhid, M.A. (ed.) Machine Learning, Big Data, and IoT for Medical Informatics Academic Press. pp. 405-419
An interpretable semi-supervised classifier using rough sets for amended self-labeling
Grau, I., Sengupta, D., Garcia Lorenzo, M.M. and Nowe, A. 2020. An interpretable semi-supervised classifier using rough sets for amended self-labeling. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020). Glasgow, UK 19 - 24 Jul 2020 IEEE . https://doi.org/10.1109/fuzz48607.2020.9177549
Genomic Variant Classifier Tool
Grau, I., Sengupta, D., Farid, D.M., Manderick, B., Nowe, A., Garcia Lorenzo, M.M., Daneels, D., Bonduelle, M., Croes, D. and Van Dooren , S. 2016. Genomic Variant Classifier Tool. SAI Intelligent Systems Conference 2016. London 21 Sep 2016 Springer. https://doi.org/10.1007/978-3-319-56994-9_32
GeVaCT - Genomic Variant Classifier Tool
Daneels, D., Grau, I., Sengupta, D., Bonduelle, M.L., Farid, D., Croes, D., Nowé, A. and Van Dooren, S. 2016. GeVaCT - Genomic Variant Classifier Tool. European Journal of Human Genetics. 24 (E-Supplement 1), p. 341.
Grey-Box Model: An ensemble approach for addressing semi-supervised classification problems
Sengupta, D., Grau, I., Garcia Lorenzo, M.M. and Nowe, A. 2016. Grey-Box Model: An ensemble approach for addressing semi-supervised classification problems. Benelearn 2016: Belgian-Dutch Conference on Machine Learning. Katholieke Universiteit Leuven, Campus Kortrijk (KULAK) 12 - 13 Sep 2016
GEVACT: Genomic Variant Classifier Tool
Daneels, D., Grau, I., Sengupta, D., Bonduelle, M., Farid, D.M., Croes, D., Nowé, A. and Van Dooren, S. 2015. GEVACT: Genomic Variant Classifier Tool. BeSHG & NVHG First Joint Meeting “Genetics & Society”. Leuven, Belgium 04 - 05 Feb 2016
CliniPhenome: Clinical and Phenotypic Annotation Database
Sengupta, D., Croes, D., Van Dooren, S., Bonduelle, M. and Nowe, A. 2015. CliniPhenome: Clinical and Phenotypic Annotation Database. BeMGI Annual Meeting 2015. Ghent, Belgium
GeVaCT: Genomic Variant Classifier Tool
Grau, I., Daneels, D., Van Dooren, S., Bonduelle , M., Farid, D.M., Croes, D., Nowé, A. and Sengupta, D. 2015. GeVaCT: Genomic Variant Classifier Tool. 10th Benelux Bioinformatics Conference. University of Antwerp, Belgium 07 - 08 Dec 2015
CliniPhenome: Clinical and Phenotypic Annotation Database
Sengupta, D., Croes, D., Van Dooren, S., Bonduelle, M. and Nowe, A. 2015. CliniPhenome: Clinical and Phenotypic Annotation Database. 2nd International Conference on Health Informatics & Technology. Valencia, Spain 27 - 29 Jul 2015 OMICS Publishing Group.
Benchmarking pre-processing and batch effect removal methods for Insilico DB: Genomics Big Data Infrastructure
De Clerck, Q., Nowe, A., Coletta, A. and Sengupta, D. 2014. Benchmarking pre-processing and batch effect removal methods for Insilico DB: Genomics Big Data Infrastructure. 9th Benelux Bioinformatics Conference (BBC 2014). Novotel-Kirchberg, Luxembourg 08 - 09 Dec 2014
Homology Modeling of Bacteriocins: From sequence alignments to structural models
Atri, P., Sengupta, D., Verma, S., Ali, S. and Dey, G. 2014. Homology Modeling of Bacteriocins: From sequence alignments to structural models. International Journal of Scientific & Engineering Research. 5 (5), pp. 123-126.
Association rule mining based study for identification of clinical parameters akin to occurrence of brain tumor
Sengupta, D., Sood, M., Vijayvargia, P., Hota, S. and Naik, P.K. 2013. Association rule mining based study for identification of clinical parameters akin to occurrence of brain tumor. Bioinformation. 9 (11), pp. 555-559. https://doi.org/10.6026/97320630009555
Design of dimensional model for clinical data storage and analysis
Sengupta, D., Arora, P., Pant, S. and Naik, P.K. 2013. Design of dimensional model for clinical data storage and analysis. Applied Medical Informatics. 32 (2), pp. 47-53.
SN algorithm: analysis of temporal clinical data for mining periodic patterns and impending augury
Sengupta, D. and Naik, P.K. 2013. SN algorithm: analysis of temporal clinical data for mining periodic patterns and impending augury. Journal of Clinical Bioinformatics. 3 24. https://doi.org/10.1186/2043-9113-3-24
TpPred: A Tool for Hierarchical Prediction of Transport Proteins Using Cluster of Neural Networks and Sequence Derived Features
Jain, S., Ranjan, P., Sengupta, D. and Naik, P.K. 2012. TpPred: A Tool for Hierarchical Prediction of Transport Proteins Using Cluster of Neural Networks and Sequence Derived Features. International Journal for Computational Biology. 1 (1), pp. 28-36.
Mode of interaction of calcium oxalate crystal with human phosphate cytidylyltransferase 1: a novel inhibitor purified from human renal stone matrix
Pathak, P., Naik, P.K., Sengupta, D., Singh, S.K. and Tandon, C. 2011. Mode of interaction of calcium oxalate crystal with human phosphate cytidylyltransferase 1: a novel inhibitor purified from human renal stone matrix. Journal of Biomedical Science and Engineering. 4 (9), pp. 591-598. https://doi.org/10.4236/jbise.2011.49075
Docking-MM-GB/SA and ADME screening of HIV-1 NNRTI inhibitor: Nevirapine and its analogues
Sengupta, D., Verma, D. and Naik, P.K. 2008. Docking-MM-GB/SA and ADME screening of HIV-1 NNRTI inhibitor: Nevirapine and its analogues. In Silico Biology. 8 (3-4), pp. 275-289.
Binding Modes, binding Affinities and ADME Screening of HIV-1 NNRTI Inhibitor: Efavirnez and its analogues.
Sengupta, D. 2007. Binding Modes, binding Affinities and ADME Screening of HIV-1 NNRTI Inhibitor: Efavirnez and its analogues. Online Journal of Bioinformatics. 8 (1), pp. 99-114.
In-silico TAT-PTD prediction for cell penetrating peptides
Tandon, C., Aggarwal, A., Goel, P., Sengupta, D. and Naik, P.K. 2007. In-silico TAT-PTD prediction for cell penetrating peptides. Online Journal of Bioinformatics. 8 (1), pp. 115-138.
Docking mode of delvardine and its analogues into the p66 domain of HIV-1 reverse transcriptase: Screening using molecular mechanics-generalized born/surface area and absorption, distribution, metabolism and excretion properties
Sengupta, D., Verma, D. and Naik, P.K. 2007. Docking mode of delvardine and its analogues into the p66 domain of HIV-1 reverse transcriptase: Screening using molecular mechanics-generalized born/surface area and absorption, distribution, metabolism and excretion properties. Journal of Biosciences. 32 (3), pp. 1307-1316. https://doi.org/10.1007/s12038-007-0140-y
Docking mode of delvardine and its analogues into the p66 domain of HIV-1 reverse transcriptase: screening using molecular mechanics-generalized born/surface area and absorption, distribution, metabolism and excretion properties
Sengupta, D., Verma, D. and Naik, P.K. 2007. Docking mode of delvardine and its analogues into the p66 domain of HIV-1 reverse transcriptase: screening using molecular mechanics-generalized born/surface area and absorption, distribution, metabolism and excretion properties. Journal of Biosciences. 32, pp. 1307-1316. https://doi.org/10.1007/s12038-007-0124-y
Clustering of HIV-I Subtype: Study of Molecular Diversity using Phylogenetic Analysis
Sengupta, D., Verma, D., Mishra, V.S. and Naik, P.K. 2006. Clustering of HIV-I Subtype: Study of Molecular Diversity using Phylogenetic Analysis. Bioinformatics Trends: A Journal of Bioinformatics and its Applications. 1 (1), pp. 1-12.
Permalink - https://westminsterresearch.westminster.ac.uk/item/vw21x/training-set-edition-using-rough-set-theory-for-semi-supervised-classification