Concatenated Decision Paths Classification for Datasets with Small Number of Class Labels

In recent years, the amount of collected information has rapidly increased, that has led to an increasing interest to time series data mining and in particular to the classification of these data. Traditional methods for classification are based mostly on distance measures between the time series and 1-NN classification. Recent development of classification methods based on time series shapelets-propose using small subsections of the entire time series, which appears to be most representative for certain classes. In addition, the shapelets-based classification method produces higher accuracies on some datasets because the global features are more sensitive to noise than the local ones. Despite its advantages the shapelets methods has an apparent disadvantage-slow training time. Varieties of algorithms were proposed to tackle this problem, one of which is the concatenated decision paths (CDP) algorithm. This algorithm as initially proposed works only with datasets with a number of class indexes higher than five. In this paper, we investigate the possibility to use CDP for datasets with less than five classes. We also introduce improvements that shorten the overall training time of the CDP method.