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Ensemble Decision Tree Classifier For Breast Cancer Data

ISSN : 2231 - 153X
Journal from gdlhub / 2017-08-14 11:52:34
Oleh : D.Lavanya and K.Usha Rani, Sree Padmavathi Mahila Visvavidyalayam, International Journal of Information Technology Convergence and Services
Dibuat : 2012-07-02, dengan 1 file

Keyword : Data mining, Classification, Decision Trees, Ensemble, Bagging, Breast Cancer datasets.
Subjek : Ensemble Decision Tree Classifier For Breast Cancer Data
Url : http://airccse.org/journal/ijitcs/papers/0212ijitcs03.pdf
Sumber pengambilan dokumen : Internet

Data mining is the process of analyzing large quantities of data and summarizing it into useful


information. In medical diagnoses the role of data mining approaches increasing rapidly. Particularly


Classification algorithms are very helpful in classifying the data, which is important in decision making


process for medical practitioners. Further to enhance the classifier accuracy various pre-processing


techniques and ensemble techniques were developed. In this study a hybrid approach, CART classifier


with feature selection and bagging technique has been considered to evaluate the performance in terms of


accuracy and time for classification of various breast cancer datasets.

Deskripsi Alternatif :

Data mining is the process of analyzing large quantities of data and summarizing it into useful


information. In medical diagnoses the role of data mining approaches increasing rapidly. Particularly


Classification algorithms are very helpful in classifying the data, which is important in decision making


process for medical practitioners. Further to enhance the classifier accuracy various pre-processing


techniques and ensemble techniques were developed. In this study a hybrid approach, CART classifier


with feature selection and bagging technique has been considered to evaluate the performance in terms of


accuracy and time for classification of various breast cancer datasets.

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