| dc.description.abstract |
In recent years, mobile device become more popular. And due to convenient transportation, people have higher probability to visit strange places. It is not easy to find a point of interest in a strange places, so we need to provide an electronic map system for users. It is not enough to provide name search for users only, because the users may not know the exact name of points. They may just want to find a specific category of point, so a good electronic map system needs to provide category search service.
In order to provide category search services, we need to classify all the points in the system. Because the system has many points, each item has one or more categories, so this is a large-scale multi-label classification problem. There are many kind of categories, we follow the categories defined by Chinese yellow pages. The category consists two levels. There are 29 categories in level 1and 1,287 in level 2. Because the number of points and categories are large, we need to spend much time for training classifiers and testing data. We reduce the dimension of categories to speed up training and testing.
After the experiment, our method’s training time and testing time are superior to the general SVM classification, the performance in level 1 Micro-F1 is 0.813, in level 2 Micro-F1 is 0.718 all slightly lower than SVM in level 1 Micro-F1 is 0.842. In level 2 Micro-F1 is 0.783. We want to try Reweighting, Downsampling to improve performance, but the performance is not wall in large-scale data. | en_US |