A Decision Tree Algorithm for Uncertain Data
K. Anuradha1, N. Tulasi Radha2, T. Pavan Kumar3
1K.Anuradha, M.CA., M.Tech Kaushik Engineering College, (A.P), India.
2N.Tulasi Radha, M.Tech, Kaushik Engineering College, (A.P.), India.
3T. Pavan Kumar, M.CA, M.Tech, Kaushik Engineering College, (A.P.), India.
Manuscript received on March 02, 2012. | Revised Manuscript received on March 31, 2012. | Manuscript published on April 30, 2012. | PP: 112-115 | Volume-1 Issue-4, April 2012 | Retrieval Number: D0293041412/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Classification is a classical problem in machine learning and data mining. Given a set of training data tuples, each having a class label and being represented by a feature vector, the task is to algorithmically build a model that predicts the class label of an unseen test tuple based on the tuple’s feature vector. One of the most popular classification models is the decision tree model. Decision trees are popular because they are practical and easy to understand. Rules can also be extracted from decision trees easily. Tree learning algorithms can generate decision tree models from a training data set. When working on uncertain data or probabilistic data, the learning and prediction algorithms need handle the uncertainty cautiously, or else the decision tree could be unreliable and prediction results may be wrong. This paper presents a new decision tree algorithm for handling uncertain data.
Keywords: Classification, Decision tree, Prediction, Uncertain data.