Dynamic & Attribute Weighted KNN for Document Classification Using Bootstrap Sampling

Although publicly accessible databases containing speech documents. It requires a great deal of time and effort required to keep them up to date is often burdensome. In an effort to help identify speaker of speech if text is available, text-mining tools, from the machine learning discipline, it can be applied to help in this process also. Here, we describe and evaluate document classification algorithms i.e. a combo pack of text mining and classification. This task asked participants to design classifiers for identifying documents containing speech related information in the main literature, and evaluated them against one another. Expected systems utilizes a novel approach of k -nearest neighbour classification and compare its performance by taking different values of k. Keywords— Data Mining, Text Mining, Classification, K-nearest neighbour, KNN