Unsupervised Feature Evaluation : A Neuro-Fuzzy Approach  

Oleh:

Basak, J. ; Pal, S. K. ; De, R. K.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 2 (2000), page 366-376.
Topik: evaluation; feature evaluation; neuro - fuzzy approach

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

Demonstrates a way of formulating neuro - fuzzy approaches for both feature selection and extraction under unsupervised learning. A fuzzy feature evaluation index for a set of features is defined in terms of degree of similarity between two patterns in both the original and transformed feature spaces. A concept of flexible membership function incorporating weighted distance is introduced for computing membership values in the transformed space. Two new layered networks are designed. The tasks of membership computation and minimization of the evaluation index, through unsupervised learning process, are embedded into them without requiring the information on the number of clusters in the feature space. The network for feature selection results in an optimal order of individual importance of the features. The other one extracts a set of optimum transformed features, by projecting n-dimensional original space directly to n' - dimensional (n' < n) transformed space, along with their relative importance. The superiority of the networks to some related ones is established experimentally.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali