Jamming Effect of Stand-Off Jammer to Main Lobe of LPI Radar

Abstract

The classification of temporal medical data has received considerable attention, because it can reveal useful information hidden within a database. The objective of models for temporal medical data is to increase the classification accuracy. However, the problem with classifying temporal medical data for each patient is that we cannot deal with multiple values for each measurement, and classifiers cannot deal with multiple classes for each patient. This is because measurements in patient case series consist of a sequence of measurements taken at ordered points in time. This paper presents a novel inner distance combination transform method that converts temporal medical data into a new feature values that is then fed into a naive Bayes classifier. We compare the new transform method with mean, median, mode and the final treatment result. The experimental results show that the proposed method is more accurate than the other methods.