| dc.description.abstract |
In recent years, the rise of e-commerce, the international demand for freight traffic increases, although the whole, the export trade volume is shrinking, the international trade volume of goods is increasing year by year. In this era of rapid demand, the quantity of goods delivered by air is relatively large, and how quickly the air cargo will be transported quickly and correctly, and the capacity of each warehousing industry is tested. Since past operating habits are based on human experience, the goods are not be able to enter the automated storage equipment and be placed in the appropriate location. Moreover, because of the flow of personnel and operating experience can not be fully inherited, export operations require a lot of manpower and time to find goods. The purpose of this study includes the use of data exploration and supervised machine learning technology to excavate the short time of the export of goods inventory forecast model, the establishment of air cargo export time warehouse model. In particular, single and multiple classification techniques are compared in order to find the optimal model and provide relevant companies with a reference to the industry concerned in forecasting the time of export of goods by air.
In this study, different classification techniques were constructed by the Weka data mining software. Particularly, the decision tree (J48), support vector machine, neural network, and nearest neighbor were used for the single classification techniques. On the other hand, the bagging and AdaBoost methods are employed to construct the multiple classifiers for comparisons.
Experimental results show that in the case of 2015 training data set, the best single classifier is the nearest neighbor algorithm whereas the multiple classifiers are the nearest neighbor algorithm by bagging and decision tree by AdaBoost. More specifically, the receiver operating characteristic curves (ROC) of these classifiers generally reach 0.74, 0.8 or so, with a good reference. Therefore, this study suggests that for the future of the case company in the air cargo export time forecast, they can give the priority to employ a single classification technology based on the nearest neighbor algorithm and multiple nearest neighbor classifiers by bagging the and multiple J48 classifies by AdaBoost to carry out air cargo export time forecasting analysis. | en_US |