Performance Evaluation of Kernels in Support Vector Machine Abstract -Recently, the Support Vector Machine (SVM) algorithm becomes very common technique that developed for pattern classification. This technique has been employed in many fields such as bioinformatics and with different attributes of datasetsfor instance numeric, nominal or mixed. One of the significant issues that user faces when implementing the SVM is choosing the appropriate kernel function with attributes of datasetto be investigated. This paper studied the behavior of SVM in regarding to the used attributes of dataset with different kernel functions. It analyzed the influence of various datasets descriptions on efficiency of (SVM)classification.SVM with these kernels have been implemented in Matlab. The investigated kernel functions are linear, polynomials, Sigmoid and Radial Based Function (RBF) . The evaluation process shows that the description of dataset with the used kernel function affects the performance of SVM classifier. Generally, SVM with linear and RBF achieved 100% in classification process when Mushroom dataset is used, and 99% when Sickle Cell Disease (SCD) is used. Keywords: SVM classifier, Linear, Polynomials, Sigmoid, Radial Based Function (RBF), Numeric, Nominal, Mixed. I. INTRODUCTION Support Vector Machine (SVM) is supervised classification technique which is based on theory of statistical learning developed by Vladimir N. [1]. SVM is the best algorithm among algorithms that was developed for pattern classification. Recently It has been adapted for other uses, such as finding regression and distribution estimation. It used in many fields such as bioinformatics, which is currently a very active research area in many universities and research institutes. SVM plays significant role in classification process. The process of analyzing dataset items according to their labels named classification, where labels are the class information of related data included in that datasets. Classification technique is used in many research areas for instance bioscience, economy and forecasting because it strengths the decisions and summarize data which were collected previously to be analyzed efficiently. Accordingly, using this technique is not limited to computer scientist but also economists, doctors [2][3][4]. The basic concept of SVM is based on binary classification as it separates data points by straight line to classify the class label. Whereas, in some datasets, it is not possible to use one straight line to separate the data points. Kernel functions are introduced to overcome the previous issue [5] [6]. This paper studies the performance of SVM method based on its kernel functions by using various types of datasets. Four different Kernel functions were investigated based on using three datasets which having verity instances of classes. The studied kernel functions are linear, polynomials, Sigmoid and Radial Based Function. The main aim of this study is explain the difference of SVM performance in term of used kernel function and dataset. In addition to, compare the investigated kernel function in term of: accuracy, recall and precision. The reminder of this paper is structured as following: Section 2 presentes related work. Support Vector Machines (SVM) is described in section 3. Dataset is described in section 4. Section 5 illustrates the results. Conclusion is detailed in section 6. II. RELATED WORK Few researches have been investigated the performance of SVM in association with the known kernel functions. The following describes some of these researches: In [7] the authors reviewed the efficacy of support vector machines regarding applied kernel functions in addition to investigating the dimensionality approaches which are fixed slope regressionand principal component analysis. The used dataset was high-dimensional bio-imaging and the results show that RBF is the best. The Authors in [8] implemented SVM depending on different four kernel-functions which are linear, polynomial, RBF and sigmoid. The implementation were used the dataset of spambase. The performance of SVM with all kernel functions were evaluated using different parameters such as Support Vector Classification SVC. The results show that the performance of SVM was the best with linear kernel function and SVC. In [9] the authors investigated the performance of SVM when using different kernel functions. This study has been performed on Arabic alphabetic character images which Intisar Shadeed Al-Mejibli University of Information Technology and Communications Baghdad,Iraq dr.intisar.almejibli@gmail.com dr.intisar.almejibli@uoitc.edu.iq Dhafar Hamed Abd Al-Maaref University College Al-Anbar, Iraq Dhafar.dhafar@gmail.com Jwan K.Alwan University of Information Technology and Communications Baghdad, Iraq jwanism@uoitc.edu.iq Abubaker Jumaah Rabash Department of Islamic Studies Sunni Endowment Diwan Al-Anbar, Iraq Abj87r@gmail.com 96 2018 1st Annual International Conference on Information and Sciences (AICIS) 978-1-5386-9188-5/18/$31.00 ©2018 IEEE DOI 10.1109/AiCIS.2018.00029