Cluster Based Boosting Techniques for Accuracy Prediction
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Date
2016
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Abstract
In recent years encroachment of social media in human life is inexplicable. The greatest and most significant issue in
latest technology is the fast retrieval of information from large databases. To overcome this, different techniques have
been developed. Improving the retrieval process and its accuracy is a vital factor in the predictive analyses of Machine
learning. Boosting is one of the techniques used for enhancing the accuracy of prediction in supervised learning. In
this process, lot of noise data may arise which adds another problem in handling training data set. Extracting accurate
data from the training dataset is an essential factor in applying clustering techniques. This paper surveys the various
boosting/clustering techniques and compares them with a motive to suggest a better environment which is free from
noise/errors so as to have maximum accuracy on the output data.