A robust likelihood approach to inference about the kappa coefficient for correlated binary data

Tsou, Tsung Shan

doi:10.1177/0962280217751519

Cited by 2 publications

(1 citation statement)

References 12 publications

(22 reference statements)

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“…Subsequently, the Kappa coefficient is used to compare the accuracies of classification and identification of the models. The closer the Kappa coefficient is to 1, the higher the consistency of classification [ 32 ]. In this study, it is found that the Kappa coefficients of the DBN-based hyperspectral image feature classification model in PaviaU, Botswana, and Cuprite database image recognition are 0.883, 0.944, and 0.972, respectively, and the Kappa coefficients all exceed 0.75; hence, the classification model of hyperspectral image features that is based on DBN has high classification accuracy.…”

Section: Discussionmentioning

confidence: 99%

Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images

Wang

Gao

et al. 2020

Computational Intelligence and Neuroscience

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To overcome the difficulty of automating and intelligently classifying the ground features in remote-sensing hyperspectral images, machine learning methods are gradually introduced into the process of remote-sensing imaging. First, the PaviaU, Botswana, and Cuprite hyperspectral datasets are selected as research subjects in this study, and the objective is to process remote-sensing hyperspectral images via machine learning to realize the automatic and intelligent classification of features. Then, the basic principles of the support vector machine (SVM) and extreme learning machine (ELM) classification algorithms are introduced, and they are applied to the datasets. Next, by adjusting the parameter estimates using a restricted Boltzmann machine (RBM), a new terrain classification model of hyperspectral images that is based on a deep belief network (DBN) is constructed. Next, the SVM, ELM, and DBN classification algorithms for hyperspectral image terrain classification are analysed and compared in terms of accuracy and consistency. The results demonstrate that the average detection accuracies of ELM on the three datasets are 89.54%, 96.14%, and 96.28%, and the Kappa coefficient values are 0.832, 0.963, and 0.924; the average detection accuracies of SVM are 88.90%, 92.11%, and 91.68%, and the Kappa coefficient values are 0.768, 0.913, and 0.944; the average detection accuracies of the DBN classification model are 92.36%, 97.31%, and 98.84%, and the Kappa coefficient values are 0.883, 0.944, and 0.972. The results also demonstrate that the classification accuracy of the DBN algorithm exceeds those of the previous two methods because it fully utilizes the spatial and spectral information of hyperspectral remote-sensing images. In summary, the DBN algorithm that is proposed in this study has high application value in object classification for remote-sensing hyperspectral images.

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Section: Discussionmentioning

confidence: 99%

Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images

Wang

Gao

et al. 2020

Computational Intelligence and Neuroscience

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Integrative approach for classifying male tumors based on DNA methylation 450K data

Wu,

Qiu,

Liu

et al. 2023

MBE

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<abstract> <p>Malignancies such as bladder urothelial carcinoma, colon adenocarcinoma, liver hepatocellular carcinoma, lung adenocarcinoma and prostate adenocarcinoma significantly impact men's well-being. Accurate cancer classification is vital in determining treatment strategies and improving patient prognosis. This study introduced an innovative method that utilizes gene selection from high-dimensional datasets to enhance the performance of the male tumor classification algorithm. The method assesses the reliability of DNA methylation data to distinguish the five most prevalent types of male cancers from normal tissues by employing DNA methylation 450K data obtained from The Cancer Genome Atlas (TCGA) database. First, the chi-square test is used for dimensionality reduction and second, L1 penalized logistic regression is used for feature selection. Furthermore, the stacking ensemble learning technique was employed to integrate seven common multiclassification models. Experimental results demonstrated that the ensemble learning model utilizing multiple classification models outperformed any base classification model. The proposed ensemble model achieved an astonishing overall accuracy (ACC) of 99.2% in independent testing data. Moreover, it may present novel ideas and pathways for the early detection and treatment of future diseases.</p> </abstract>

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A robust likelihood approach to inference about the kappa coefficient for correlated binary data

Cited by 2 publications

References 12 publications

Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images

Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images

Integrative approach for classifying male tumors based on DNA methylation 450K data

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