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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | INNOVATIVE APPROACH TO CANCER DIAGNOSIS THROUGH HYBRID WRAPPER-FILTER FEATURE SELECTION |
| Authors Name: | M.AKILA , C.AJITHA |
| Download E-Certificate: | Download |
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IJNRD_215066
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| Published Paper Id: | IJNRD2403172 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | Feature selection, as a preprocessing step to machine learning, is effective in reducing dimensionality, removing irrelevant data and increasing learning accuracy. The development of microarray dataset technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it helps us to exactly predict and diagnose cancer. To precisely classify cancer we have to select genes related to cancer. The challenging task in cancer diagnosis is how to identify salient expression genes from thousands of genes in microarray data because extracted genes from microarray dataset have many unwanted datas not related to cancer. In this project we attempt explore a novel hybrid wrapper and filter feature selection algorithm for classification problem using a memetic framework i.e., a combination of genetic algorithm (GA) and local search (LS) has been proposed.. The LS is performed using correlation based filter methods are discritize, ranking and redundancy elimination with symmetrical uncertainty (SU) measure .using this hybrid method we can able find cancer related gene, From the larger amount of gene datas .using that smaller dataset doctors can able to find the affected gene and provide better treatment. The efficiency and the effectiveness of the method are demonstrated through extensive comparisons with other methods using real-world datasets of high dimentionality |
| Keywords: | Feature Selection, Memetic Algorithm, Filters ,wrappers, genetic algorithm, symmetrical uncertainty. |
| Cite Article: | "INNOVATIVE APPROACH TO CANCER DIAGNOSIS THROUGH HYBRID WRAPPER-FILTER FEATURE SELECTION", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.b652-b656, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403172.pdf |
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2403172 Registration ID: 215066 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: b652-b656 Country: MADURAI, TAMILNADU, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403172 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403172 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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