Classification and Optimization Scheme for Text Data using Machine Learning Naïve Bayes Classifier

Text classification is an essential advance in characteristic dialect processing. It very well may be performed utilizing different classification algorithms. Hadoop Map Reduce is widely utilized in text classification to perform classification on colossal measure of text data. However, Map Reduce required a ton of time to perform the tasks thereby increasing latency and since the data is distributed over the cluster it builds time and thus reducing processing speed. Also Hadoop utilizes long queue of code. Motivated by this, we propose a basic yet compelling machine learning method which uses Naïve Bayes classifier for text data. In Machine Learning approach, the classifier is built automatically by learning the properties of categories from a set of pre-defined training data. Hence, it can process complex furthermore, multi assortment information in dynamic situations. Here we propose a naïve bayes classifier which scales directly with number of indicators and data points which can be used for both binary and multiclass classification problems. We implemented the presented schemes using Machine Learning tool. The experimental results demonstrate the performance improvement in the classification technique.