Edge distance graph kernel and its application to small molecule   classification

MEHMET TAN

Makale detayları

Edge distance graph kernel and its application to small molecule classification

Yazarlar:

MEHMET TAN

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 3, pp.2479-2490

Belge Tipi:

Makale

Yayın Tarihi:

5/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000740935

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

Graph classification is an important problem in graph   mining with various applications in different fields. Kernel methods have   been successfully applied to this problem, recently producing promising   results. A graph kernel that mostly specifies classification performance has   to be defined in order to apply kernel methods to a graph classification   problem. Although there are various previously proposed graph kernels, the   problem is still worth investigating, as the available kernels are far from   perfect. In this paper, we propose a new graph kernel based on a recently   proposed concept called edge distance-k graphs. These new graphs are derived   from the original graph and have the potential to be used as novel graph   descriptors. We propose a method to convert these graphs into a multiset of   strings that is further used to compute a kernel for graphs. The proposed   graph kernel is then evaluated on various data sets in comparison to a   recently proposed group of graph kernels. The results are promising, both in   terms of performance and computational requirements.

Makale detayları

Edge distance graph kernel and its application to small molecule classification

Yazarlar: MEHMET TAN

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 3, pp.2479-2490

Belge Tipi: Makale

Yayın Tarihi: 5/2017

Belge Dili: Türkçe

Document ID (Org No.): 2000740935

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

MEHMET TAN

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 3, pp.2479-2490

Belge Tipi:

Makale

Yayın Tarihi:

5/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000740935

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632