This paper provides a method of the process of computation called the cumulative method, it is based upon repeated cumulative process. The cumulative method is being adapted to the purposes of computation, particularl...This paper provides a method of the process of computation called the cumulative method, it is based upon repeated cumulative process. The cumulative method is being adapted to the purposes of computation, particularly multiplication and division. The operations of multiplication and division are represented by algebraic formulas. An advantage of the method is that the cumulative process can be performed on decimal numbers. The present paper aims to establish a basic and useful formula valid for the two fundamental arithmetic operations of multiplication and division. The new cumulative method proved to be more flexible and made it possible to extend the multiplication and division based on repeated addition/subtraction to decimal numbers.展开更多
Purpose:The aim of this research is to propose a modification of the ANOVA-SVM method that can increase accuracy when detecting benign and malignant breast cancer.Methodology:We proposed a new method ANOVA-BOOTSTRAP-S...Purpose:The aim of this research is to propose a modification of the ANOVA-SVM method that can increase accuracy when detecting benign and malignant breast cancer.Methodology:We proposed a new method ANOVA-BOOTSTRAP-SVM.It involves applying the analysis of variance(ANOVA)to support vector machines(SVM)but we use the bootstrap instead of cross validation as a train/test splitting procedure.We have tuned the kernel and the C parameter and tested our algorithm on a set of breast cancer datasets.Findings:By using the new method proposed,we succeeded in improving accuracy ranging from 4.5 percentage points to 8 percentage points depending on the dataset.Research limitations:The algorithm is sensitive to the type of kernel and value of the optimization parameter C.Practical implications:We believe that the ANOVA-BOOTSTRAP-SVM can be used not only to recognize the type of breast cancer but also for broader research in all types of cancer.Originality/value:Our findings are important as the algorithm can detect various types of cancer with higher accuracy compared to standard versions of the Support Vector Machines.展开更多
文摘This paper provides a method of the process of computation called the cumulative method, it is based upon repeated cumulative process. The cumulative method is being adapted to the purposes of computation, particularly multiplication and division. The operations of multiplication and division are represented by algebraic formulas. An advantage of the method is that the cumulative process can be performed on decimal numbers. The present paper aims to establish a basic and useful formula valid for the two fundamental arithmetic operations of multiplication and division. The new cumulative method proved to be more flexible and made it possible to extend the multiplication and division based on repeated addition/subtraction to decimal numbers.
文摘Purpose:The aim of this research is to propose a modification of the ANOVA-SVM method that can increase accuracy when detecting benign and malignant breast cancer.Methodology:We proposed a new method ANOVA-BOOTSTRAP-SVM.It involves applying the analysis of variance(ANOVA)to support vector machines(SVM)but we use the bootstrap instead of cross validation as a train/test splitting procedure.We have tuned the kernel and the C parameter and tested our algorithm on a set of breast cancer datasets.Findings:By using the new method proposed,we succeeded in improving accuracy ranging from 4.5 percentage points to 8 percentage points depending on the dataset.Research limitations:The algorithm is sensitive to the type of kernel and value of the optimization parameter C.Practical implications:We believe that the ANOVA-BOOTSTRAP-SVM can be used not only to recognize the type of breast cancer but also for broader research in all types of cancer.Originality/value:Our findings are important as the algorithm can detect various types of cancer with higher accuracy compared to standard versions of the Support Vector Machines.
