A fast nearest neighbor classifier based on self-organizing incremental neural network

• Published in: Neural networks Vol. 21; no. 10; pp. 1537 - 1547
• Main Authors: Shen, Furao, Hasegawa, Osamu
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.12.2008; Elsevier
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Computer science; control theory; systems; Connectionism. Neural networks; Exact sciences and technology; Fast; Information, signal and communications theory; Nearest neighbor; Neural Networks (Computer); Normal Distribution; Pattern Recognition, Automated - methods; Prototype-based classifier; Self-organizing incremental neural network; Signal and communications theory; Signal representation. Spectral analysis; Signal, noise; Telecommunications and information theory; Self-organizing incremental neural network; Nearest neighbor; Prototype-based classifier; Fast; Performance evaluation; Nearest neighbour; Automatic classification; Prototype; Neural network; Experimental study; Signal classification; Velocity; Learning; Acquisition process; Self organization; Compression ratio; Performance; Signal to noise ratio
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2008.07.001

A fast prototype-based nearest neighbor classifier is introduced. The proposed Adjusted SOINN Classifier (ASC) is based on SOINN (self-organizing incremental neural network), it automatically learns the number of prototypes needed to determine the decision boundary, and learns new information without destroying old learned information. It is robust to noisy training data, and it realizes very fast classification. In the experiment, we use some artificial datasets and real-world datasets to illustrate ASC. We also compare ASC with other prototype-based classifiers with regard to its classification error, compression ratio, and speed up ratio. The results show that ASC has the best performance and it is a very efficient classifier.