Journal of Health & Medical Informatics

ISSN: 2157-7420

Open Access

Megha Purohit


  • Research Article
    Kernel Oriented Multivariate Feature Selection for Breast Cancer Data Classification via MRMR Filter
    Author(s): Pooja Mehta and Megha PurohitPooja Mehta and Megha Purohit

    A feature selection technique is highly preferred preceding data classification to improve prediction performance especially in the high dimensional space. In general, filter techniques can be considered as essential or assistant selection system on account of their effortlessness, adaptability, and low computational many-sided quality. Nonetheless, a progression of inconsequential cases demonstrates that filter techniques result in less precise execution since they disregard the conditions of features. Albeit few publications have committed their regard for uncover the relationship of features by multivariate-based techniques, these strategies depict connections among elements just by linear techniques. While straightforward linear combination relationship limits the transformation in execution. In this paper, we utilized kernel method for svm-RFE with MRMR way to deal with find inal.. Read More»
    DOI: 10.4172/2157-7420.1000239

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