Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Multiple Criteria Mathematical Programming and Data Mining
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
All of Statistics: A Concise Course in Statistical Inference
All of Statistics: A Concise Course in Statistical Inference
Classification for Orange Varieties Using Near Infrared Spectroscopy
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
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A multivariate technique and feasibility of using near infrared spectroscopy (NIRS) for non-destructive discriminating Thai orange varieties were studied in this paper. Short-wavelength near infrared (SW-NIR) spectra in region of 643 to 970nm were collected from 100 orange sample of each varieties. A total of 300 spectra were used to develop an accurate classification model by diversity of classifiers. The result showed that Logistic Regression (LGR) model was achieved 100% classification accuracy while Multi-Criteria Quadratic Programming (MCQP) and Support Vector Machine (SVM) ones also demonstrated satisfying result (95%). In order to find simpler and easier interpretable classification model, several feature selection techniques were evaluated to identify the most relevant wavelengths to the orange varieties. With four principal components (PCs) from Principal Component Analysis (PCA) and the effective wavelengths of 769.68, 692.28, 662.61 and 959.31nm from Least Square Forward Selection (LS-FS), the reduced classification models of LGR also achieved satisfying classification accuracy respectively. Furthermore, both Kernel Principal Component Analysis (KPCA) and Kernel Least Square Forward Selection (KLS-FS) with SVM enhanced performance of models by 5 PCs and features respectively. The result concluded that NIRS can yield an accurate classification for Thai tangerine varieties from whole spectra and can enhance interpretability of classification model by feature subset.