Classification of Time-Sequential Attributes by Using Sequential Pattern Rules
Ya‐Han Hu, Yen‐Liang Chen, Er-Hsuan Lin · 2007
Classification is an important method for predicting class labels of samples. Although attribute-values in many real-life applications may change over time, existing classification research usually assumes that attribute-values are static. In this paper, we extend the traditional classification problem to deal with time-sequential attributes whose values may change over time. Accordingly, an algorithm called MultipleMIS-SP is presented to generate all classification rules for the classifier generation. Two scoring functions are proposed to predict class labels using our classifier. Detailed experiments are also presented. The results show that the accuracy of MultipleMIS-SP is greater than the traditional classification technique C4.5 algorithm in both the synthetic datasets and the real dataset.