A Novel Cluster of Quarter Feature Selection Based on Symmetrical Uncertainty

Sai Prasad POTHARAJU; Marriboyina SREEDEVI

Makale detayları

A Novel Cluster of Quarter Feature Selection Based on Symmetrical Uncertainty

Yazarlar:

Sai Prasad POTHARAJU | Marriboyina SREEDEVI

Yayıncı:

Gazi University

Dergi:

Gazi University Journal of Science 2018 Vol.31 Issue 2, pp.456-470

Belge Tipi:

Makale

Yayın Tarihi:

5/2018

Belge Dili:

Türkçe

Document ID (Org No.):

2000428210

eISSN:

2147-1762

Özet:

 


Due to the diversity of sources, a large amount of data is being produced and also has various

problems including mislabeled data, missing values, imbalanced class labels, noise and high

dimensionality. In this research article, we proposed a novel framework to address high

dimensionality issue with feature reduction to increase the classification performance of various

lazy learners, rule-based induction, bayes, and tree-based models. In this research, we proposed

robust Quarter Feature Selection (QFS) framework based on Symmetrical Uncertainty Attribute

Evaluator. Our proposed technique analyzed with Six real world datasets. The proposed

framework , divide whole data space into 4 sets (Quarters) of features without duplication. Each

such quarter has less than or equals 25 % features of whole data space. Practical results recorded

that, one of the quarter, sometimes more than one quarter recording improved accuracy than the

already available feature selection methods in the literature. In this research, we used filter-based

feature selection methods such as GRAE, IG, CHI-SQUARE (CHI 2), Relief to compare the

quarter of features produced by proposed technique.

.

Makale detayları

A Novel Cluster of Quarter Feature Selection Based on Symmetrical Uncertainty

Yazarlar: Sai Prasad POTHARAJU | Marriboyina SREEDEVI

Yayıncı: Gazi University

Dergi: Gazi University Journal of Science 2018 Vol.31 Issue 2, pp.456-470

Belge Tipi: Makale

Yayın Tarihi: 5/2018

Belge Dili: Türkçe

Document ID (Org No.): 2000428210

eISSN: 2147-1762

Sayı

Yazarlar:

Sai Prasad POTHARAJU | Marriboyina SREEDEVI

Yayıncı:

Gazi University

Dergi:

Gazi University Journal of Science 2018 Vol.31 Issue 2, pp.456-470

Belge Tipi:

Makale

Yayın Tarihi:

5/2018

Belge Dili:

Türkçe

Document ID (Org No.):

2000428210

eISSN:

2147-1762