Читать книгу Intelligent Data Analytics for Terror Threat Prediction - Группа авторов - Страница 26

Support vector machine (SVM) is a one of the best machine learning algorithm used for both classification and regression, widely it is used for classify given data points even though those input vectors are mapped non-linearly [8]. In social networks data available in many forms so to detect rumors it is required to classify given text data using classification algorithms based on dataset features. Classifying dataset which has multiple features and multiple dimensions is a challenging task, so using SVM will give better results.

The main objective of the SVM is to find a hyperplane in an N-dimensional space that distinctly classifies the data points. Rumor detection in social networks is mainly depending on text classification, using SVM algorithm it can be done. It is shown in Figure 1.5, how a SVM classifier classifies the given dataset that has multiple features and dimensions. SVM classifies as large margin in between two types of data: first one is in circle shape and the second one is in triangle shape. These two data points have been classified with maximum distance (thick line) between them. The large margin shown in Figure 1.5(a) says that it is classifying those circles and triangles equally from that point, which means distance between those two data types is maximum through that margin. As shown in Figure 1.5(b), SVM also supports multi-dimensional data.

Figure 1.5 Hyperplane in 2-D and 3-D.