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Gas Identi cation by Using a Cluster-k-Nearest-Neighbor

Brahim Belhaouari, samir (2009) Gas Identi cation by Using a Cluster-k-Nearest-Neighbor. International Conference on Machine Learning and Computing .

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Abstract

Abstract. Among the most serious limitations facing the success of future consumer gas identification systems are the drift problem and the real-time detection due to the slow response of most of todays gas sensors. In this paper, a novel gas identification approach based on Cluster-k-Nearest Neighbor. The effectiveness of this approach has been suc-cessfully demonstrated on an experimentally obtained data set. Our classify takes advantage of both k-NN which is highly accurate and K-means cluster which is able to reduce the time of classification, we introduce Cluster-k-Nearest Neighbor as “variable k”-NN dealing with the centroid or mean point of all subclasses generated by clustering algo-rithm. In general the algorithm of Kmeans cluster is not stable in term of accuracy. Therefore for that reason we develop another algorithm for clustering space which contributes a higher accuracy compares to K-means cluster with less subclass number, higher stability and bounded time of classification with respect to the variable data size. We find 98.7% of accuracy in the classification of 6 different types of Gas by using K-means cluster algorithm and we find almost the same by using the new clustering algorithm.

Item Type:Article
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:5895
Deposited By: Dr Samir Brahim Belhaouari
Deposited On:07 Jul 2011 07:03
Last Modified:19 Jan 2017 08:25

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