A Research Travelogue on Feature Sub Set Selection Algorithms

Authors

  • R. Ravikumar Research Scholar, Department of Computer Science, Krishna University, Machilipatnam, Andhra Pradesh, India
  • M. Babu Reddy Assistant Professor, Department of Computer Science, Krishna University, Machilipatnam, Andhra Pradesh, India

Keywords:

Classification, Clustering, Feature Selection, Machine Learning, SVM

Abstract

In machine learning as the dimensionality of the data rises, the amount of data required to provide a reliable analysis grows exponentially. To perform dimensionality reduction on high-dimensional micro array data, many
different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. Analyzing microarrays can be difficult due to the size of the data they provide. In addition the complicated relations among the different genes make analysis more difficult and removing excess features can improve the quality of the results. Feature selection has been an active and fruitful field of research area in pattern recognition, machine learning, statistics and data mining communities. The main objective of this paper is feature selection is to choose a subset of input variables by eliminating features.

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Published

15-11-2019

How to Cite

Ravikumar, R., & Babu Reddy, M. (2019). A Research Travelogue on Feature Sub Set Selection Algorithms. Asian Journal of Computer Science and Technology, 8(3), 35–37. Retrieved from https://ojs.trp.org.in/index.php/ajcst/article/view/2739