Shrinkage Linear with Quadratic Gaussian Discriminant Analysis for Big Data Classification
نویسندگان
چکیده
Generation of massive data is increasing in big industries due to the evolution modern technologies. The include source from sensors, Internet Things, digital and social media. In particular, these systems consist extraction, preprocessing, integration, analysis, visualization mechanism. encountered sources are redundant, incomplete conflict. Moreover, real time applications, it a tedious process for interpretation all different sources. this paper, gathered preprocessed handle issues such as For that, proposed have generalized dimensionality reduction technique called Shrinkage Linear Discriminate Analysis (SLDA). As result, (LDA) will improve performance classifier with generalization. Even though, classifier, irrelevant features get degraded by system further. Hence, relevant most important selected using Pearson correlation-based feature selection which selects subset correlated improving classification system. classified Quadratic-Gaussian Discriminant (QGDA) classifier. techniques tested localization cover sets machine learning University California Irvine (UCI) repository. addition on datasets evaluated evaluation metrics compared other similar methods prove efficiency It has achieved better performance. acquired accuracy over 91% experiment datasets. Based results terms training percentage mapper, meaningful conclude that method could be used classification.
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ژورنال
عنوان ژورنال: Intelligent Automation and Soft Computing
سال: 2022
ISSN: ['2326-005X', '1079-8587']
DOI: https://doi.org/10.32604/iasc.2022.024539