Fast Balanced K-means (FBK-means) clustering approach is one of the most important consideration ... more Fast Balanced K-means (FBK-means) clustering approach is one of the most important consideration when one want to solve clustering problem of balanced data. Mostly, numerical experiments show that FBK-means is faster and more accurate than the K-means algorithm, Genetic Algorithm, and Bee algorithm. FBK-means Algorithm needs few distance calculations and fewer computational time while keeping the same clustering results. However, the FBK-means algorithm doesn’t give good results with imbalanced data. To resolve this shortage, a more efficient clustering algorithm, namely Fast K-means (FK-means), developed in this paper. This algorithm not only give the best results as in the FBK-means approach but also needs lower computational time in case of imbalance data.
Support Vector Machine (SVM) is an effective model for many classification problems. However, SVM... more Support Vector Machine (SVM) is an effective model for many classification problems. However, SVM needs the solution of a quadratic program which require specialized code. In addition, SVM has many parameters, which affects the performance of SVM classi?er. Recently, the Generalized Eigenvalue Proximal SVM (GEPSVM) has been presented to solve the SVM complexity. In real world applications data may affected by error or noise, working with this data is a challenging problem. In this paper, an approach has been proposed to overcome this problem. This method is called DSA-GEPSVM. The main improvements are carried out based on the following: 1) a novel fuzzy values in the linear case. 2) A new Kernel function in the nonlinear case. 3) Differential Search Algorithm (DSA) is reformulated to ?nd near optimal values of the GEPSVM parameters and its kernel parameters. The experimental results show that the proposed approach is able to find the suitable parameter values, and has higher classification accuracy compared with some other algorithms.
Fast Balanced K-means (FBK-means) clustering approach is one of the most important consideration ... more Fast Balanced K-means (FBK-means) clustering approach is one of the most important consideration when one want to solve clustering problem of balanced data. Mostly, numerical experiments show that FBK-means is faster and more accurate than the K-means algorithm, Genetic Algorithm, and Bee algorithm. FBK-means Algorithm needs few distance calculations and fewer computational time while keeping the same clustering results. However, the FBK-means algorithm doesn’t give good results with imbalanced data. To resolve this shortage, a more efficient clustering algorithm, namely Fast K-means (FK-means), developed in this paper. This algorithm not only give the best results as in the FBK-means approach but also needs lower computational time in case of imbalance data.
Support Vector Machine (SVM) is an effective model for many classification problems. However, SVM... more Support Vector Machine (SVM) is an effective model for many classification problems. However, SVM needs the solution of a quadratic program which require specialized code. In addition, SVM has many parameters, which affects the performance of SVM classi?er. Recently, the Generalized Eigenvalue Proximal SVM (GEPSVM) has been presented to solve the SVM complexity. In real world applications data may affected by error or noise, working with this data is a challenging problem. In this paper, an approach has been proposed to overcome this problem. This method is called DSA-GEPSVM. The main improvements are carried out based on the following: 1) a novel fuzzy values in the linear case. 2) A new Kernel function in the nonlinear case. 3) Differential Search Algorithm (DSA) is reformulated to ?nd near optimal values of the GEPSVM parameters and its kernel parameters. The experimental results show that the proposed approach is able to find the suitable parameter values, and has higher classification accuracy compared with some other algorithms.
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