Weighted Support Vector Machine Classification by using Confidence Parameters for Unbalanced Sampling Target Data Sets

Artificial Intelligent plays a vital role in Machine Learning. ML is one of the subfields of AI. In Support Vector Machine algorithm developed based on nonlinear SV in the period of sixties in Russia is called Generalized Algorithm of SVM. SVM first founded by Vapnik. In a classification of SVM for unbalanced datasets perform in original SVM and Weighted Support Vector Machine using Target data set (WSVMA). SVMs give effective solutions for datasets that are balanced, in a similar way they give effectiveness to unbalanced datasets to create sub-optimal models. When creating the unbalanced training sets, where the data of the Target class are exceeded by the data present in the class of the Non-Target class the accomplishment of the SVM classifier is so enhanced. In sequence to balance the distribution of the target class, our algorithm study is based on density information in training sets to eliminate unwanted data of the non-target class and bring about new artificial data of the target class. The main idea is for the unbalanced datasets to assign the different sets of weighted values to different sets of data points such that they called the Weighted Support Vector Machine algorithm for the training data set (WSVM) by using the "Confidence parameter, C". In this training, the algorithm finds out the decision surface according to the correlative set of data points in the training phase. Artificial Intelligent (AI) plays a vital role in Machine Learning (ML). Support Vector Machine (SVM) algorithm works in the size of margin bounds on unseen data set of hyperplane. It's mainly associated with the process of calculation based on the mathematical procedure and memory space. SVMs play an important and effective role in the nonlinear ML algorithm. SVMs produce sub-optimal models whenever the datasets are severely unbalanced, but they do provide effective solutions whenever the datasets are balanced. The approach of assigning various weights to various sets of data can also be used to compute the confidence factor. SVM learning problems maintain a strong approximation solution in a faster technique, minimize the time provided, as well as provide great speed.