An Experimental Study on Hypothyroid Using Rotation Forest

Gaikwad, Sheetal; Pise, Nitin

doi:10.5121/ijdkp.2014.4603

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Each tree is trained on the data sets present in the transformed feature space (Xia, Du, He, & Chanussot, 2014). Other studies related to image classification support the claim that the classification logic and application of the RTF approach provides more accurate results when compared to other ensemble methods (Du, Samat, Waske, Liu, & Li, 2015;Gaikwad & Pise, 2014;Kuncheva & Rodriguez, 2007;Lasota, Łuczak, & Trawiński, 2012;Liu & Huang, 2008;Xia, 2016;Xia et al, 2014).…”

Section: Introductionmentioning

confidence: 76%

Mapping land use with using Rotation Forest algorithm from UAV images

Akar

2017

European Journal of Remote Sensing

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The aim of this study is to test the performance of the Rotation Forest (RTF) algorithm in areas that have similar characteristics by using Unmanned Aerial Vehicle (UAV) images for the production of most up-to-date and accurate land use maps. The performance of the RTF algorithm was compared to other ensemble methods such as Random Forest (RF) and Gentle AdaBoost (GAB). The accuracy assessments showed that the RTF with 84.90% and 93.33% accuracies provided better performance than RF (7% and 4%) and GAB (15% and 11%) in urban and rural areas, respectively. Subsequently, in order to increase the classification accuracy, a majority filter was applied to post-classification images and the overall classification accuracy of the RFT was increased approximately up to 3%. Also, the results of classification were also analysed using the McNemar test. Consequently, this study shows the success of the RTF algorithm in the classification of UAV images for land use mapping.

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Section: Introductionmentioning

confidence: 76%

Mapping land use with using Rotation Forest algorithm from UAV images

Akar

2017

European Journal of Remote Sensing

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“…They analyzed misclassified data and the wrapper feature in various ways, obtaining the best result of 99.63% accuracy for the hypothyroidism dataset. Additionally, they evaluated the effectiveness of the rotating forest algorithm in diagnosing thyroid disorders [27].…”

Section: Research Backgroundmentioning

confidence: 99%

Imbalanced Multiclass Medical Data Classification based on Learning Automata and Neural Network

Soleimani,

Forouzanfar,

Soltani

et al. 2023

EAI Endorsed Trans AI Robotics

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Data classification in the real world is often faced with the challenge of data imbalance, where there is asignificant difference in the number of instances among different classes. Dealing with imbalanced data isrecognized as a challenging problem in data mining, as it involves identifying minority-class data with ahigh number of errors. Therefore, the selection of unique and appropriate features for classifying data withsmaller classes poses a fundamental challenge in this research. Nowadays, due to the widespread presenceof imbalanced medical data in many real-world problems, the processing of such data has gained attentionfrom researchers. The objective of this research is to propose a method for classifying imbalanced medicaldata. In this paper, the hypothyroidism dataset from the UCI repository is used. In the feature selection stage,a support vector machine algorithm is used as a cost function, and the wrapper algorithm is employed asa search strategy to achieve an optimal subset of features. The proposed method achieves high accuracy,reaching 99.6% accuracy for data classification through the optimization of a neural network using learningautomata.

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“…The Lasso (Least Absolute Shrinkage and Selection Operator), which was originally proposed by Tibshirani for linear regression models, has become a popular model selection and shrinkage estimation method [37]. The motivation comes from Breiman's NNG (Non-negative Garrote) [4] in 1995 as Suggest that we have a data (X i ,yi),i=1,2,…n, where X i =(xi1,…,xip) and yi are design matrix and response variable respectively. Assume that…”

Section: Lasso-logistic Regressionmentioning

confidence: 99%

Prediction of Hypothyroidism Disease by Data Mining Technique

Khiew¹,

Wang²,

Lin³

et al. 2021

Journal of Data Science

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Background: Hypothyroidism is one of the endocrine diseases found in human being, it is not a immediate fatal disease, but progress in chronic status that lead to other diseases. Several machine learning techniques were applied to hypothyroidism for the prediction of hypothyroid medical diseases. Methods: The dataset used for UCI repository which has 3163 observations with 151 belongs to category hypothyroid disease. Considered with two missing data method, four imbalance data method and two kinds of classification models by using serval evaluation indexes to find a better model. Results: After comparison of these models, query_on_thyroxine and lithium were ignored and TBG was reconsidered in the model. A new RF imputation method was used. Finally, all variables model have 100% accuracy but cost time and money, only use TSH, FTI, TBG, TT4 and age variables model also can keep 0.9988 AUC, which will more useful in real case. Conclusion: Both all variables mode and five variables model have very high accuracy than previous studies, a nonparametric ensemble model is suggest in this case. But there also have some limitations like out-of-date data, hypothyroidisms type were not considered.

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An Experimental Study on Hypothyroid Using Rotation Forest

Abstract: ABSTRACT

Cited by 6 publications

References 12 publications

Mapping land use with using Rotation Forest algorithm from UAV images

Mapping land use with using Rotation Forest algorithm from UAV images

Imbalanced Multiclass Medical Data Classification based on Learning Automata and Neural Network

Prediction of Hypothyroidism Disease by Data Mining Technique

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