scholarly journals Supervised classification methods applied to airborne hyperspectral images: comparative study using mutual information

2019 ◽  
Vol 148 ◽  
pp. 97-106
Author(s):  
Hasna Nhaila ◽  
Asma Elmaizi ◽  
Elkebir Sarhrouni ◽  
Ahmed Hammouch
Keyword(s):  
Mutual Information ◽  
Comparative Study ◽  
Supervised Classification ◽  
Hyperspectral Images ◽  
Classification Methods ◽  
Supervised Classification Methods

scholarly journals On Appraisal of Spectral Features Based Supervised Classifications for Hyperspectral Images

2020 ◽  
Vol 8 (6) ◽  
pp. 593-600
Keyword(s):  
Spectral Data ◽  
Execution Time ◽  
Supervised Classification ◽  
Performance Metrics ◽  
Hyperspectral Images ◽  
Spectral Features ◽  
Classification Methods ◽  
Spectral Bands ◽  
Supervised Classification Methods

The classification of hyperspectral images is a challenging task since it contains unbalanced ratio between the training and testing samples, and number of spectral bands. The detailed spectral data of hyperspectral images increases the ability to individualize the different classes and achieving accurate classification maps. Hence, in this paper, we use spectral data for classification and we address the performance of different supervised classification techniques like logic-based, ensemble-based, statistical-based, non-probabilistic-based and instance-based techniques on spectral features. Experiments are carried out using hyperspectral imagery captured by AVIRIS sensor such as Indian Pines, Salinas and Salinas-A. The appraisal of these supervised classification methods are held with each other in terms of performance metrics such as overall accuracy, precision, recall, F1-score and execution time.

Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Nadège Dossat ◽  
Alain Mangé ◽  
Jérôme Solassol ◽  
William Jacot ◽  
Ludovic Lhermitte ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Supervised Classification ◽  
Protein Profiling ◽  
Clinical Proteomics ◽  
High Dimensional ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Supervised Classification Methods

A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested.

A Comparison of Semi-Supervised Classification Approaches for Software Defect Prediction

2014 ◽  
Vol 23 (1) ◽  
pp. 75-82 ◽  
Author(s):  
Cagatay Catal
Keyword(s):  
Supervised Classification ◽  
Defect Prediction ◽  
Support Vector ◽  
Software Defect Prediction ◽  
Classification Methods ◽  
Data Set ◽  
Software Defect ◽  
Data Points ◽  
Supervised Classification Methods ◽  
Prediction Approach

AbstractPredicting the defect-prone modules when the previous defect labels of modules are limited is a challenging problem encountered in the software industry. Supervised classification approaches cannot build high-performance prediction models with few defect data, leading to the need for new methods, techniques, and tools. One solution is to combine labeled data points with unlabeled data points during learning phase. Semi-supervised classification methods use not only labeled data points but also unlabeled ones to improve the generalization capability. In this study, we evaluated four semi-supervised classification methods for semi-supervised defect prediction. Low-density separation (LDS), support vector machine (SVM), expectation-maximization (EM-SEMI), and class mass normalization (CMN) methods have been investigated on NASA data sets, which are CM1, KC1, KC2, and PC1. Experimental results showed that SVM and LDS algorithms outperform CMN and EM-SEMI algorithms. In addition, LDS algorithm performs much better than SVM when the data set is large. In this study, the LDS-based prediction approach is suggested for software defect prediction when there are limited fault data.

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