One-Class-at-a-Time Removal Sequence Planning Method for Multiclass Classification Problems

Using dynamic programming, this work develops a one-class-at-a-time removal sequence planning method to decompose a multiclass classification problem into a series of two-class problems. Compared with previous decomposition methods, the approach has the following distinct features. First, under the one-class-at-a-time framework, the approach guarantees the optimality of the decomposition. Second, for a K-class problem, the number of binary classifiers required by the method is only K-1. Third, to achieve higher classification accuracy, the approach can easily be adapted to form a committee machine. A drawback of the approach is that its computational burden increases rapidly with the number of classes. To resolve this difficulty, a partial decomposition technique is introduced that reduces the computational cost by generating a suboptimal solution. Experimental results demonstrate that the proposed approach consistently outperforms two conventional decomposition methods

A Mobile Learning Support System for Ubiquitous Learning Environments

AbstractThis paper proposes a Mobile Learning Support System (MLSS) which enables students to access learning materials by utilizing 2D barcodes and GPS technology. As the pilot system of ubiquitous learning, we used camera-equipped mobile phones and 2D barcode tags to obtain learning information from online websites. By installing the MLSS on to their mobile phones, students can scan the tag attached to the corresponding object to display related multimedia materials on the screen of mobile phones. Furthermore, MLSS also applies GPS technology to develop a location-aware environment for students. GPS technology is used to detect the students’ location and identify which 2D barcode tags are in their proximity. Therefore, this paper provides the opportunity to develop for developers create ubiquitous learning environments that combine real-world and digital world resources