NDLI: Agarwood classification: A Case-Based Reasoning approach based on E-nose

Content Provider	IEEE Xplore Digital Library
Author	Najib, M.S. Ahmad, M.U. Funk, P. Taib, M.N. Ali, N.A.M.
Copyright Year	2012
Description	Author affiliation: School of Innovation, Design and Engineering, Malardalen University, PO Box 883, SE-721 23, Vasteras, Sweden (Ahmad, M.U.; Funk, P.) \|\| Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450, Selangor, Malaysia (Najib, M.S.; Taib, M.N.) \|\| Forest Research Institute Malaysia, 52109, Selangor, Malaysia (Ali, N.A.M.)
Abstract	Using an array of sensors (E-nose) to classify Agarwood has proven to be successful and produced performance close to an expert level (90% of expert level performance) but it has proven difficult to eliminate misclassifications without over-fitting. In our effort to improve our result we explored a self-improving Case-Based Reasoning approach and reached 100% correct classification. Case-Based Reasoning is an approach that will learn from every new classified case and hence the risk for misclassification is reduced. Also when new cases have to be classified that have never occurred before the system will avoid misclassification (similarity measurement is low). The approach also enables indeterminism; in reality a sample may be both close to a good case and a bad case and need further exploration by experts. The approach also handles natural variants in the wood samples well; both low-quality and high-quality samples may spread considerably in the context of E-nose readings and there is no model available of low or high quality.
Starting Page	120
Ending Page	126
File Size	2295126
Page Count	7
File Format	PDF
ISBN	9781467309608
e-ISBN	9781467309615
DOI	10.1109/CSPA.2012.6194703
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-23
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	classifications Accuracy Agarwood Sensor phenomena and characterization Feature extraction case-based reasoning Libraries Vectors e-nose feature selection Sensor arrays
Content Type	Text
Resource Type	Article

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