Committee classifier based on linear discriminant analysis for the detection of radioisotopes from airborne gamma-ray spectra

Dess, Brian W.; Small, Gary W.

doi:10.1016/j.jenvrad.2020.106217

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…Classification systems have been used intensively to obtain the prediction of categorical responses for several areas such as bioinformatics [1][2], chemistry [3][4], medicine [5][6][7], ecology [8], and finance [9]. Due to the importance, the researchers aim to increase the accuracy of the classification results by improving the computational part of the classifiers.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Classification System Based on Kernel Principal Component Analysis and Data Complexity Measures

2023

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Principal component analysis is commonly used as a pre-step before employing a classifier to avoid the negative effect of the dimensionality and multicollinearity. The performance of a classifier is severely affected by the deviations from the linearity of the data structure and noisy samples. In this paper, we propose a new classification system that overcomes the drawback of these crucial problems, simultaneously. Our proposal is relying on the kernel principal component analysis with a proper parameter selection approach with data complexity measures. According to the empirical results, F1, T2 and T3 in AUC, T3 in GMEAN and T2 and T3 in MCC performed better than classical and other complexity measures. Comparison of classifiers showed that Radial SVM performs better in AUC, and KNN performs better in GMEAN and MCC using KPCA with complexity measures. As a result, our proposed system produces better results in various classification algorithms with respect to classical approach.

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