Two-stage SVM Research Articles

Sentiment Analysis Pedulilindungi Tweet Using Support Vector Machine Method

Pedulilindungi application has many benefits but many controversies arise in the community. Various opinions in the form of tweets were expressed by the public, both positive and negative opinions. In this study, the objective is to make a classification model to classify tweets into two types of sentiment, namely positive and negative. The model is made in several stages, namely data retrieval, data filtering, data labeling, data preprocessing, splitting data train and data test, feature selection using Information Gain and Genetic Algorithm, and then classification using the SVM method. The model using two-stage feature selection and SVM method, obtained an accuracy value of 64.08% with 841 features and processing time of 0.033 seconds with 9.6% CPU usage. The model with two-stage feature selection is more efficient and effective than the one-stage feature selection model whose accuracy value is only 60.56% with 1800 features and a processing time of 0.044 seconds with 15.4% CPU usage.

IDSS-based Two stage classification of brain tumor using SVM

The computer and image processing has a significant role in detecting tumor area. The decision support systems for human brain MR images are essentially encouraged with the requirement of attaining maximal achievable efficiency and the motivation of the approach which is to enhance the performance of Computer-Aided Diagnosis (CAD) system to detect a tumor in the human brain. Even though numerous support systems have been introduced in the past, this is still an open problem seeking for an accurate and robust decision support system. The Interactive Diagnosis Support System (IDSS) approach has addressed the limitations of nonillumination and low contrast of a brain tumor MR image that influences the procedure of accurate image classification. Thus, the IDSS is implemented in three phases namely image preprocessing for enhancing non-illuminated features, feature extraction and image classification which is accomplished using two-stage interactive SVM Classification. The local binary patterns are detected in the feature extraction for accurate classification of usual and unusual brain MR Images. The experimental outcomes for this approach are carried out using MATLAB R2016a and evaluated using the brain images downloaded from the Internet. The performance metrics such as structured similarity index, sensitivity, specificity and accuracy were used to assess the IDSS-based tumor classification system. When compared with the traditional classifiers such as ANFIS, Backpropagation and K-NN, the IDSS approach has significant brain tumor classification accuracy.

Research on regional clustering and two-stage SVM method for container truck recognition

With large-scale, integrated, intelligence for ports, many ports begin to use intelligent detection systems to make their operations more efficient. The container truck recognition and positioning system is also beginning to apply into container quayside to assist the joint operations between quay cranes and container trucks. However, the traditional vehicle detection by using motion region detection cannot recognize the type of moving object, and the traditional pattern recognition method cannot meet the requirements in real-time operation. In order to solve these problems, an algorithm fused by regional clustering and two-stage SVM classifier is proposed in this paper. The method consists of two phases, which are independently executed in two camera systems on quay cranes. In the first stage, a fast motion regional clustering algorithm is used to detect moving image patches as the truck candidate sub-windows. In the second stage, the container trucks will be recognized in these sub-windows by an optimized two-stage SVM classifier. Compared with existing traditional algorithm, experimental results in container terminal show that the fusion algorithm with regional clustering and two-stage SVM has higher efficiency and better truck recognition performance.

Recognizing Human Actions in Basketball Video Sequences on the Basis of Global and Local Pairwise Representation

A feature-representation method for recognizing actions in sports videos on the basis of the relationship between human actions and camera motions is proposed. The method involves the following steps: First, keypoint trajectories are extracted as motion features in spatio-temporal sub-regions called “spatio-temporal multiscale bags” (STMBs). Global representations and local representations from one sub-region in the STMBs are then combined to create a “glocal pairwise representation” (GPR). The GPR considers the co-occurrence of camera motions and human actions. Finally, two-stage SVM classifiers are trained with STMB-based GPRs, and specified human actions in video sequences are identified. An experimental evaluation of the recognition accuracy of the proposed method (by using the public OSUPEL basketball video dataset and broadcast videos) demonstrated that the method can robustly detect specific human actions in both public and broadcast basketball video sequences.

A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC

Membrane proteins play important roles in many biochemical processes and are also attractive targets of drug discovery for various diseases. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. Recently we developed a novel system for predicting protein subnuclear localizations. In this paper, we propose a simplified version of our system for predicting membrane protein types directly from primary protein structures, which incorporates amino acid classifications and physicochemical properties into a general form of pseudo-amino acid composition. In this simplified system, we will design a two-stage multi-class support vector machine combined with a two-step optimal feature selection process, which proves very effective in our experiments. The performance of the present method is evaluated on two benchmark datasets consisting of five types of membrane proteins. The overall accuracies of prediction for five types are 93.25% and 96.61% via the jackknife test and independent dataset test, respectively. These results indicate that our method is effective and valuable for predicting membrane protein types. A web server for the proposed method is available at http://www.juemengt.com/jcc/memty_page.php

Fast classification for large data sets via random selection clustering and Support Vector Machines

Support Vector Machines SVMs are high-accuracy classifiers. However, normal SVM algorithms are unsuitable for classification of large data sets because of their training complexity. In this paper, we propose a novel SVM classification approach for large data sets. We first use the random selection to select a small group of training data for the first-stage SVM. Then a de-clustering technique is proposed to recover the training data for the second-stage SVM. This two-stage SVM classifier has distinctive advantages on dealing with huge data sets such as those in bioinformatics. The performance analysis is also given in this paper. Finally, we apply the proposed method on several benchmark problems. Experimental results demonstrate that this approach has good classification accuracy while the training is significantly faster than other SVM classifiers.