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Emphasizing Minority Class in LDA for Feature Subset Selection on
High-Dimensional Small-Sized Problems
Abstract:
Although...
produce feature subsets with excellent performance in both classification
and robustness. Further experimental results of ...
during evaluating a feature subset in feature selection, therefore, the
influence of the minority class should be enhanced...
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Emphasizing minority class in lda for feature subset selection on high dimensional small-sized problems

Emphasizing minority class in lda for feature subset selection on high dimensional small-sized problems
+91-9994232214,8144199666, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com

IEEE PROJECTS 2015-2016
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Contact:+91-9994232214,+91-8144199666
Email:ieeeprojectchennai@gmail.com

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Emphasizing minority class in lda for feature subset selection on high dimensional small-sized problems

  1. 1. Emphasizing Minority Class in LDA for Feature Subset Selection on High-Dimensional Small-Sized Problems Abstract: Although mostly used for pattern classification, linear discriminant analysis (LDA) can also be used in feature selection as an effective measure to evaluate the separative ability of a feature subset. When applied to feature selection on high-dimensional small sized (HDSS) data (generally) with class-imbalance, LDA encounters four problems, including singularity of scatter matrix, over fitting, overwhelming and prohibitively computational complexity. In this study, we propose the LDA-based feature selection method MCELDA (minority class emphasized linear discriminant analysis) with a new regularization technique to address the first three problems. Different to giving equal or more emphasis to majority class in conventional forms of regularization, the proposed regularization emphasizes more on minority class, with the expectation of improving overall performance by alleviating overwhelming of majority class to minority class as well as over fitting in minority class. In order to reduce computational overhead, an incremental implementation of LDA-based feature selection has been introduced. Comparative studies with other forms of regularization to LDA as well as with other popular feature selection methods on five HDSS problems show that MCE-LDA can
  2. 2. produce feature subsets with excellent performance in both classification and robustness. Further experimental results of true positive rate (TPR) and true negative rate (TNR) have also verified the effectiveness of the proposed technique in alleviating overwhelming and over fitting problems. Existing System: A typical set-based feature selection algorithm runs recursively imbedding two major components in its recursive procedure. The first component is a candidate feature subsets searching or generating strategy and the second is an evaluation criterion that measures the goodness of candidate feature subsets generated. In the first component, the sequential forward searching (SFS) and sequential backward searching (SBS) are usually employed. In the second component, two types of evaluation criteria including classifier-dependent and classifier-independent measures are generally utilized. Proposed System: We propose the minority class emphasized linear discriminant analysis (MCELDA) containing a new form of regularization to LDA. Instead of giving more emphasis to class with majority of samples as done in the conventional forms of regularization such as shrinking individual scatter matrices towards the pooled scatter matrix, our new regularization emphasizes more on the minority class. The rationale behind our regularization is that in situation of small sample size and class imbalance, the minority class is prone to be overlooked
  3. 3. during evaluating a feature subset in feature selection, therefore, the influence of the minority class should be enhanced. Our experimental studies show that MCE-LDA produces feature subsets leading to improvements in both classification performance and robustness performance. Hardware Requirements: • System : Pentium IV 2.4 GHz. • Hard Disk : 40 GB. • Floppy Drive : 1.44 Mb. • Monitor : 15 VGA Colour. • Mouse : Logitech. • RAM : 256 Mb. Software Requirements: • Operating system : - Windows XP. • Front End : - JSP • Back End : - SQL Server Software Requirements: • Operating system : - Windows XP. • Front End : - .Net
  4. 4. • Back End : - SQL Server

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Emphasizing minority class in lda for feature subset selection on high dimensional small-sized problems +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

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