Optimal Predictive Model for Large Scale Classification
Anna Joshy1, Leya Elizabeth Sunny2, Linda Sara Mathew3
1Anna Joshy*, Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India.
2Leya Elizabeth Sunny, Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India.
3Linda Sara Mathew, Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India.
Manuscript received on January 07, 2021. | Revised Manuscript received on January 18, 2021. | Manuscript published on January 30, 2021. | PP: 130-133 | Volume-9 Issue-5, January 2021. | Retrieval Number: 100.1/ijrte.E5208019521 | DOI: 10.35940/ijrte.E5208.019521
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Biosensors calculate the expression pattern of multi- ple genes in experimental work. A unique genomic chip is possible to produce levels of expression from multiple genes. The ability to interpret these high-dimensional samples fuels the creation of methods of automated analysis. Even though the existing methods undergo imbalanced problems and less classification accuracy over gene expression datasets.Therefore, a novel computational method has been developed inorder to increase the classification performance of gene expression dataset and accurate disease prediction.By adding fuzzy memberships, we take into account the features of imbalanced data. Within our work, both the sample entropies and the expense for each class decide the fuzzy memberships in order to understand the different samples with various contributors to the judgment boundary. Thus, on imbalanced genomic datasets, the current proposed approach will result in more desirable classification outcomes. In addition, to build a new algorithm, we integrate the fuzzy memberships into current MKL. The results show that the proposed approach will tackle the imbalanced problem and achieve high accuracy rate.
Keywords: Biosensers, Imbalance problem, Fuzzy membership, Multiple kernel learning.