Unsupervised feature evaluation: a neuro-fuzzy approach

• Published in: IEEE transactions on neural networks Vol. 11; no. 2; pp. 366 - 376
• Main Authors: Pal, S.K., De, R.K., Basak, J.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2000
• Subjects: Artificial neural networks; Computation; Computer networks; Data mining; Embedded computing; Extraterrestrial measurements; Feature extraction; Fuzzy set theory; Fuzzy sets; Learning; Networks; Neural networks; Optimization; Pattern recognition; Similarity; Unsupervised learning
• ISSN: 1045-9227; 1941-0093
• DOI: 10.1109/72.839007

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.