Abstract
Problem statement: Research on Smooth Support Vector Machine (SSVM) is an active field in data mining. Many researchers developed the method to improve accuracy of the result. This study proposed a new SSVM for classification problems. It is called Multiple Knot Spline SSVM (MKS-SSVM). To evaluate the effectiveness of our method, we carried out an experiment on Pima Indian diabetes dataset. The accuracy of previous results of this data still under 80% so far. Approach: First, theoretical of MKS-SSVM was presented. Then, application of MKS-SSVM and comparison with SSVM in diabetes disease diagnosis were given. Results: Compared to the SSVM, the proposed MKS-SSVM showed better performance in classifying diabetes disease diagnosis with accuracy 93.2%. Conclusion: The results of this study showed that the MKS-SSVM was effective to detect diabetes disease diagnosis and this is very promising compared to the previously reported results.
Highlights
Support Vector Machines (SVM) is a new algorithm of data mining technique, recently received increasing popularity in machine learning and statistics community
SVM have become the tool of choice for fundamental classification problem of machine learning and data mining
In Polat et al.[8] a cascade learning system based on Generalized Discriminant Analysis (GDA) and Least Square Support Vector Machien (LS-SVM) was used
Summary
Support Vector Machines (SVM) is a new algorithm of data mining technique, recently received increasing popularity in machine learning and statistics community. Unlike traditional methods which minimize the empirical training error, SVM aims at minimizing an upper bound of the generalization error through maximizing the margin between the separating hyperplane and the data This can be regarded as an approximate implementation of the structure risk minimization principle[1,2]. SSVM is a development of SVM that uses smoothing technique This method was first introduced by Lee[3] in 2001. We propose a new smooth function to approximate the plus function. This function is called Multiple Knot Spline function which is a modification of the spline function[5]. The used data source is Pima Indian diabetes disease taken from the UCI machine learning repository[6] This dataset is commonly used among researchers that use machine learning methods for diabetes disease classification. The results were compared with the results of the previous studies reported[8,9,10,11]
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