Optimal feature selection in the classification of synchronous fluorescence of petroleum oils

Khalid J. Siddiqui; DeLyle Eastwood

doi:10.1117/12.236185

25 March 1996 Optimal feature selection in the classification of synchronous fluorescence of petroleum oils

Khalid J. Siddiqui, DeLyle Eastwood

Proceedings Volume 2705, Fluorescence Detection IV; (1996) https://doi.org/10.1117/12.236185
Event: Photonics West '96, 1996, San Jose, CA, United States

Abstract

Pattern classification of UV-visible synchronous fluorescence of petroleum oils is performed using a composite system developed by the authors. The system consists of three phases, namely, feature extraction, feature selection and pattern classification. Each of these phases are briefly reviewed, focusing particularly on the feature selection method. Without assuming any particular classification algorithm the method extracts as much information (features) from spectra as conveniently possible and then applies the proposed successive feature elimination process to remove the redundant features. From the remaining features a significantly smaller, yet optimal, feature subset is selected that enhances the recognition performance of the classifier. The successive feature elimination process and optimal feature selection method are formally described. These methods are successfully applied for the classification of UV-visible synchronous fluorescence spectra. The features selected by the algorithm are used to classify twenty different sets of petroleum oils (the design set). A proximity index classifier using the Mahalanobis distance as the proximity criterion is developed using the smaller feature subset. The system was trained on the design set. The recognition performance on the design set was 100%. The recognition performance on the testing set was over 93% by successfully identifying 28 out of 30 samples in six classes. This performance is very encouraging. In addition, the method is computationally inexpensive and is equally useful for large data set problems as it always partitions the problem into a set of two class problems. The method further reduces the need for a careful feature determination problem which a system designer usually encounters during the initial design phase of a pattern classifier.

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Khalid J. Siddiqui and DeLyle Eastwood "Optimal feature selection in the classification of synchronous fluorescence of petroleum oils", Proc. SPIE 2705, Fluorescence Detection IV, (25 March 1996); https://doi.org/10.1117/12.236185

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KEYWORDS

Feature extraction

Luminescence

Feature selection

Image classification

Absorption

Statistical analysis

Classification systems

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