Journal ArticleDOI
Rough-fuzzy weighted k-nearest leader classifier for large data sets
V. Suresh Babu,P. Viswanath +1 more
TLDR
A generalization over the earlier proposed k-nearest leader-based classifier where a novel soft computing approach is used to resolve the uncertainty and combined principles of rough set theory and fuzzy set theory are used to analyze the proposed method.About:
This article is published in Pattern Recognition.The article was published on 2009-09-01. It has received 30 citations till now. The article focuses on the topics: Margin classifier & Quadratic classifier.read more
Citations
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Journal ArticleDOI
Classification Techniques in Machine Learning: Applications and Issues
Aized Amin Soofi,Arshad Awan +1 more
TL;DR: The goal of this study is to provide a comprehensive review of different classification techniques in machine learning and will be helpful for both academia and new comers in the field of machine learning to further strengthen the basis of classification methods.
Journal ArticleDOI
Fuzzy-rough nearest neighbour classification and prediction
Richard Jensen,Chris Cornelis +1 more
TL;DR: This paper proposes an NN algorithm that uses the lower and upper approximations from fuzzy-rough set theory in order to classify test objects, or predict their decision value, and shows that it outperforms other NN approaches and is competitive with leading classification and prediction methods.
Journal ArticleDOI
A method for discovering clusters of e-commerce interest patterns using click-stream data
TL;DR: An improved leader clustering algorithm is constructed and three typical user interest patterns are derived based on a real click-stream dataset to provide significant assistances on webpage optimization and personalized recommendation.
Journal ArticleDOI
Hubness-based fuzzy measures for high-dimensional k -nearest neighbor classification
TL;DR: Experimental evaluation on real data from the UCI repository and the image domain suggests that the fuzzy approach provides a useful measure of confidence in the predicted labels, resulting in improvement over the crisp weighted method, as well as the standard kNN classifier.
Posted Content
An improvement to k-nearest neighbor classifier
TL;DR: This paper presents a novel improvement to the k-NNC called k-Nearest Neighbor Mean Classifier (k-NNMC), which finds k nearest neighbors for each class of training patterns separately, and finds means for each of these k neighbors (class-wise).
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Book
Self-Organizing Maps
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Journal ArticleDOI
Instance-Based Learning Algorithms
TL;DR: This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.