Toward optimum fuzzy support vector machines using error distribution. (April 2020)

• Record Type: Journal Article
• Authors: Bahraini, Tahereh; Ghazi, Saeedeh; Yazdi, Hadi Sadoghi
• Is Part Of: Engineering applications of artificial intelligence. Volume 90(2020)
• Publication Date: 2020-04
• Subjects: Classification -- Support vector machine (SVM) -- Fuzzy support vector machine (FSVM) -- Social media data -- 500px images -- Loss function; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103545
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Support vector machines (SVM) is one of the prevalent techniques in the machine learning which is applicable in many of the real world classification problems. However, these problems are sensitive to noise and the presence of the outliers in training data. For this reason, other methods like fuzzy SVM (FSVM) were introduced to solve this challenge. Similar to SVM, these methods look for finding an optimal hyperplane that separates classes with the maximum possible margin. The main difference is the allocation of fuzzy membership degree to each training sample based on its significance so that leads to the resistance of the method. In this paper to overcome this problem, we proposed a robust FSVM based on prior knowledge related to error distribution (PKED-FSVM) to classify synthetic and real data. The optimal coefficient in our proposed PKED-FSVM method is introduced from the viewpoint of minimum Bayesian risk. Our proposed method is compared with SVM and other FSVM methods and results are obtained on several types of synthetic data from UCI. Finally, we collected the social media images from 500px based on user-tagged and created two data sets; Coastal-Non-Coastal, and the Rural–urban, and used the proposed method to classify these two data sets and separating coastal areas from non-coastal areas, and urban areas from rural areas, respectively. The results show that our proposed method has acceptable performance and works better than other competing methods.