Comparison Analysis: Large Data Classification Using PLS-DA and Decision Trees

Autor:	Norashikin Nasaruddin, Kartini Kassim, Nurazlina Abdul Rashid, Amirah Hazwani Abdul Rahim
Rok vydání:	2020
Předmět:	Statistics and Probability Economics and Econometrics business.industry Data classification Decision tree Sample (statistics) Pattern recognition Artificial intelligence Statistics Probability and Uncertainty business Linear discriminant analysis Misclassification error Mathematics
Zdroj:	Mathematics and Statistics. 8:100-105
ISSN:	2332-2144 2332-2071
Popis:	Classification studies are widely applied in many areas of research. In our study, we are using classification analysis to explore approaches for tackling the classification problem for a large number of measures using partial least square discriminant analysis (PLS-DA) and decision trees (DT). The performance for both methods was compared using a sample data of breast tissues from the University of Wisconsin Hospital. A partial least square discriminant analysis (PLS-DA) and decision trees (DT) predict the diagnosis of breast tissues (M = malignant, B = benign). A total of 699 patients diagnose (458 benign and 241 malignant) are used in this study. The performance of PLS-DA and DT has been evaluated based on the misclassification error and accuracy rate. The results show PLS-DA can be considered as a good and reliable technique to be used when dealing with a large dataset for the classification task and have good prediction accuracy.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d715e3a9811af305c920bcf49b853e6e https://doi.org/10.13189/ms.2020.080205 Zobrazit plný text záznamu