An Unsupervised Approach For Selection of Candidate Feature Set Using Filter Based Techniques

Sai Prasad POTHARAJU

Makale detayları

An Unsupervised Approach For Selection of Candidate Feature Set Using Filter Based Techniques

Yazarlar:

Sai Prasad POTHARAJU

Yayıncı:

Gazi University

Dergi:

Gazi University Journal of Science 2018 Vol.31 Issue 3, pp.789-799

Belge Tipi:

Makale

Yayın Tarihi:

8/2018

Belge Dili:

Türkçe

Document ID (Org No.):

2000428162

eISSN:

2147-1762

Özet:

 


	
	
	

Clustering
is an unsupervised Data Mining approach.In this research article, we
have proposed an unsupervised approach using filter based feature
selection methods and K-Means clustering technique to derive the
candidate subset. Initially, score of each feature is recorded using
traditional filter based methods, then normalized the dataset using
Min-Max technique, then formed the unsupervised dataset. K-Means
algorithm is employed on the dataset to form the clusters of
features. To decide the strong subset, Multi Layer Perceptron(MLP) is
applied on each cluster. Based on the minimum Root Mean Square (RMS)
error rate given by MLP best cluster is selected. This framework is
compared with traditional methods over six well known datasets having
the total features in between 34 and 90 using various classification
algorithms. The proposed method has shown competitive performance
than few of the traditional methods.

.

Makale detayları

An Unsupervised Approach For Selection of Candidate Feature Set Using Filter Based Techniques

Yazarlar: Sai Prasad POTHARAJU

Yayıncı: Gazi University

Dergi: Gazi University Journal of Science 2018 Vol.31 Issue 3, pp.789-799

Belge Tipi: Makale

Yayın Tarihi: 8/2018

Belge Dili: Türkçe

Document ID (Org No.): 2000428162

eISSN: 2147-1762

Sayı

Yazarlar:

Sai Prasad POTHARAJU

Yayıncı:

Gazi University

Dergi:

Gazi University Journal of Science 2018 Vol.31 Issue 3, pp.789-799

Belge Tipi:

Makale

Yayın Tarihi:

8/2018

Belge Dili:

Türkçe

Document ID (Org No.):

2000428162

eISSN:

2147-1762