Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/334670
Title: Reliable and efficient methodologies for optimal feature selection and classification using combinatorial approaches
Researcher: Gilbert Nancy S
Guide(s): Appavu Alias Balamurugan S
Keywords: Engineering and Technology
Computer Science
Computer Science Information Systems
Neural Network
Cuckoo Search And Genetic Algorithm
Optimal Feature Selection And Classification
Feature Selection
Data Mining
University: Anna University
Completed Date: 2020
Abstract: In Biomedical data mining dataset contains huge information, which makes tedious for data processing Feature selection and classification is an important in knowledge learning analyses to make decisions in real world datasets This plays an important role of identifying as well predicting results to categorize the dataset Mainly the implementations are carried out to be supportive for bio medical datasets Feature selection and classification is well supportive for bio medical data processing in high imperative high dimensional dataset Due to various challenges in research the existing implementations are not well sufficient to predict classification accuracy To solve the classification problem we need dedicative approaches using the neural network and classification model Initially we propose a feature selection called the multiple kernel fuzzy c means clustering with rough set theory RS MKFCM algorithm and then followed by classification using Cuckoo search and Genetic Algorithm CGA based Neural Network NN classifier At first the multiple kernel fuzzy c means clustering with rough set theory RS MKFCM is projected to classify the micro array high dimensional dataset to select the important features further to apply CGA NN classifier Selected feat ures from micro array datasets are collected and fed to the neural network for training In neural network we utilize scaled conjugate gradient algorithm for training It provides faster training with excellent test efficiency To improve the classification performance hybridization of cuckoo search and genetic algorithm CGA is utilized with neural network for weight optimization process At last the experimentation is performed on the five different micro array dataset newline
Pagination: xvi, 144p.
URI: http://hdl.handle.net/10603/334670
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificates.pdf645.64 kBAdobe PDFView/Open
03_abstracts.pdf90.02 kBAdobe PDFView/Open
04_acknowledgements.pdf504.79 kBAdobe PDFView/Open
05_contents.pdf73.05 kBAdobe PDFView/Open
06_listoftables.pdf123.46 kBAdobe PDFView/Open
07_listoffigures.pdf126.52 kBAdobe PDFView/Open
08_listofabbreviations.pdf59.93 kBAdobe PDFView/Open
09_chapter1.pdf179.61 kBAdobe PDFView/Open
10_chapter2.pdf203.82 kBAdobe PDFView/Open
11_chapter3.pdf309.41 kBAdobe PDFView/Open
12_chapter4.pdf302.08 kBAdobe PDFView/Open
13_chapter5.pdf393.83 kBAdobe PDFView/Open
14_chapter6.pdf397.63 kBAdobe PDFView/Open
15_conclusion.pdf71.14 kBAdobe PDFView/Open
16_references.pdf171.68 kBAdobe PDFView/Open
17_listofpublications.pdf128.15 kBAdobe PDFView/Open
80_recommendation.pdf70.76 kBAdobe PDFView/Open
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