An Approach to Applying Multiple Linear Regression Models by Interlacing Data in Classifying Similar Software
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The development of information technology is bringing many changes to everyday life, and machine learningcan be used as a technique to solve a wide range of real-world problems. Analysis and utilization of data areessential processes in applying machine learning to real-world problems. As a method of processing data inmachine learning, we propose an approach based on applying multiple linear regression models by interlacingdata to the task of classifying similar software. Linear regression is widely used in estimation problems to modelthe relationship between input and output data. In our approach, multiple linear regression models are generatedby training on interlaced feature data. A combination of these multiple models is then used as the predictionmodel for classifying similar software. Experiments are performed to evaluate the proposed approach ascompared to conventional linear regression, and the experimental results show that the proposed methodclassifies similar software more accurately than the conventional model. We anticipate the proposed approachto be applied to various kinds of classification problems to improve the accuracy of conventional linearregression.
Abstract
The development of information technology is bringing many changes to everyday life, and machine learningcan be used as a technique to solve a wide range of real-world problems. Analysis and utilization of data areessential processes in applying machine learning to real-world problems. As a method of processing data inmachine learning, we propose an approach based on applying multiple linear regression models by interlacingdata to the task of classifying similar software. Linear regression is widely used in estimation problems to modelthe relationship between input and output data. In our approach, multiple linear regression models are generatedby training on interlaced feature data. A combination of these multiple models is then used as the predictionmodel for classifying similar software. Experiments are performed to evaluate the proposed approach ascompared to conventional linear regression, and the experimental results show that the proposed methodclassifies similar software more accurately than the conventional model. We anticipate the proposed approachto be applied to various kinds of classification problems to improve the accuracy of conventional linearregression.
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