performance analysis data mining von gupta shelly (5 Ergebnisse)

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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The present study aimed to do the performance analysis of several data mining classification techniques using three different machine learning tools over the healthcare datasets. In this study, different data mining classification techniques have been tested on four different healthcare datasets. The standards used are percentage of accuracy and error rate of every applied classification technique. The experiments are done using the 10 fold cross validation method. A suitable technique for a particular dataset is chosen based on highest classification accuracy and least error rate. 76 pp. Englisch.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gupta ShellyShelly Gupta has done B.Tech. in IT from GJUS&T, India in 2009 & M.Tech. in CSE from Banasthali University, India in 2011. She has published 3 research papers at National /Intl level with 1 yr exp. in research. Her intere.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The present study aimed to do the performance analysis of several data mining classification techniques using three different machine learning tools over the healthcare datasets. In this study, different data mining classification techniques have been tested on four different healthcare datasets. The standards used are percentage of accuracy and error rate of every applied classification technique. The experiments are done using the 10 fold cross validation method. A suitable technique for a particular dataset is chosen based on highest classification accuracy and least error rate.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 76 pp. Englisch.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The present study aimed to do the performance analysis of several data mining classification techniques using three different machine learning tools over the healthcare datasets. In this study, different data mining classification techniques have been tested on four different healthcare datasets. The standards used are percentage of accuracy and error rate of every applied classification technique. The experiments are done using the 10 fold cross validation method. A suitable technique for a particular dataset is chosen based on highest classification accuracy and least error rate.