Classification for Data of Hematopoietic Tumor Patients with Fast Block-Matching-Based Self-Organizing Map Learning in Dynamic Environments

抄録

In this paper, a scheme of recognizing hematopoietic tumor patients is presented, using self-organizing maps constructed by fast block-matching-based learning. This fast learning is referred to as T-BMSOM leaning. To classify the patients, screening data of examinees are presented to a constructed map. In T-BMSOM learning, a set of neurons arranged in square is regarded as a block, and one of the blocks is chosen as a winner per the presented data. It is assumed that members of a training data set to construct the map never change in static environments, whereas the data set is suddenly updated during learning in dynamic environments. While adopting the concept of blocks makes it possible to construct well-organized maps in dynamic environments, it lengthens the time for learning. To overcome this issue, T-BMSOM learning is based on a decision-tree-like winner search and a batch process. The screening data of an examinee frequently lacks several of the item values, and hence the data is presented to the map after averages of non-missing item values substitute for items with no values. The class of the data to be classified is basically judged by observing the label of a winner block. Simulation results establish that the proposed scheme achieves high accuracy of correctly recognizing the data of hematopoietic tumor patients, even if training the map is conducted in a dynamic environment.