Comparison of AIS and fuzzy c-means clustering methods on the classification of breast cancer and diabetes datasets

Seral ÖZŞEN; Rahime CEYLAN

Makale detayları

Comparison of AIS and fuzzy c-means clustering methods on the classification of breast cancer and diabetes datasets

Yazarlar:

Seral ÖZŞEN | Rahime CEYLAN

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2014 Vol.22 Issue 5, pp.1241-1254

Belge Tipi:

Makale

Yayın Tarihi:

4/2014

Belge Dili:

Türkçe

Document ID (Org No.):

2000742869

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

Data reduction is an indispensable part of pattern  classification processes in many cases. If the number of samples is  excessive, sample reduction or data reduction algorithms can be used for an  effective processing time and reliable successive results. Many methods have  been used for data reduction. Fuzzy c-means is one of these methods and it  is widely used in such applications as clustering algorithms. In this study,  we applied a different clustering algorithm, an artificial immune system  (AIS), for the data reduction process. We realized the performance  evaluation experiments on the standard Chainlink and Iris datasets, while  the main application was conducted using the Wisconsin Breast Cancer and  Pima Indian datasets, which were taken from the University of California,  Irvine Machine Learning Repository. For these datasets, the performance of  the AIS in the data reduction process was compared with the fuzzy c-means  clustering algorithm, in which a multilayer perceptron artificial neural  network was used as a classifier after the data reduction processes. The  obtained results show that the maximum classification accuracies were  obtained as 73.96 % for the Pima Indian Diabetes dataset and 97.80% for  the Wisconsin Breast Cancer dataset with the AIS and the compression rates  were 80% and 40% for these results. For fuzzy c-means clustering,  however, the aforementioned accuracies were obtained as 63% and 86.69%  for the Pima Indian Diabetes and Wisconsin Breast Cancer datasets,  respectively. Moreover, the compression rates for these results for fuzzy  c-means were 90% and 70%. When the mean classification accuracy values  over the experimented compression rates were taken into consideration, the  AIS reached a mean classification accuracy of 70.07% for the Pima Indian  Diabetes dataset, while 47.64% was obtained by fuzzy c-means for this  dataset. For the Wisconsin Breast Cancer dataset, however, the mean  classification accuracies of the AIS and fuzzy c-means methods were recorded  as 94.90% and 75.43%, respectively.

 

Data reduction is an indispensable part of pattern  classification processes in many cases. If the number of samples is  excessive, sample reduction or data reduction algorithms can be used for an  effective processing time and reliable successive results. Many methods have  been used for data reduction. Fuzzy c-means is one of these methods and it  is widely used in such applications as clustering algorithms. In this study,  we applied a different clustering algorithm, an artificial immune system  (AIS), for the data reduction process. We realized the performance  evaluation experiments on the standard Chainlink and Iris datasets, while  the main application was conducted using the Wisconsin Breast Cancer and  Pima Indian datasets, which were taken from the University of California,  Irvine Machine Learning Repository. For these datasets, the performance of  the AIS in the data reduction process was compared with the fuzzy c-means  clustering algorithm, in which a multilayer perceptron artificial neural  network was used as a classifier after the data reduction processes. The  obtained results show that the maximum classification accuracies were  obtained as 73.96 % for the Pima Indian Diabetes dataset and 97.80% for  the Wisconsin Breast Cancer dataset with the AIS and the compression rates  were 80% and 40% for these results. For fuzzy c-means clustering,  however, the aforementioned accuracies were obtained as 63% and 86.69%  for the Pima Indian Diabetes and Wisconsin Breast Cancer datasets,  respectively. Moreover, the compression rates for these results for fuzzy  c-means were 90% and 70%. When the mean classification accuracy values  over the experimented compression rates were taken into consideration, the  AIS reached a mean classification accuracy of 70.07% for the Pima Indian  Diabetes dataset, while 47.64% was obtained by fuzzy c-means for this  dataset. For the Wisconsin Breast Cancer dataset, however, the mean  classification accuracies of the AIS and fuzzy c-means methods were recorded  as 94.90% and 75.43%, respectively.

Makale detayları

Comparison of AIS and fuzzy c-means clustering methods on the classification of breast cancer and diabetes datasets

Yazarlar: Seral ÖZŞEN | Rahime CEYLAN

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2014 Vol.22 Issue 5, pp.1241-1254

Belge Tipi: Makale

Yayın Tarihi: 4/2014

Belge Dili: Türkçe

Document ID (Org No.): 2000742869

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Seral ÖZŞEN | Rahime CEYLAN

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2014 Vol.22 Issue 5, pp.1241-1254

Belge Tipi:

Makale

Yayın Tarihi:

4/2014

Belge Dili:

Türkçe

Document ID (Org No.):

2000742869

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632