TEACHER-STUDENT FRAMEWORK FOR LIGHT WEIGHTED ENSEMBLE CLASSIFIER COMBINED WITH DEEP NETWORK AND RANDOM FOREST AND THE CLASSIFICATION METHOD BASED ON THEREOF

摘要

The present invention relates to a teacher-student framework for lightening an ensemble classifier in which a deep network and a random forest are combined, and more specifically, as a teacher-student framework, using a data set A, a deep teacher network and a teacher random A teacher learning module for learning a teacher model composed of a forest; A soft target data generation module for inputting data set B into the deep teacher network and teacher random forest learned in the teacher learning module, and combining the output two outputs ^{to generate a soft target data set B *;} A student learning module for learning a student model composed of a student network and a student random forest by using the ^{data set B *} generated by the soft target data generation module; And a classification module for performing classification by combining two outputs of the student network and the student random forest learned in the student learning module. In addition, the present invention relates to a classification method based on a teacher-student framework for lightening an ensemble classifier combined with a deep network and a random forest, and more specifically, as a classification method based on a teacher-student framework, (1) Training a teacher model consisting of a deep teacher network and a teacher random forest using the data set A; (2) inputting the data set B to the deep teacher network and the teacher random forest learned in step (1), and combining the two outputs to generate a ^{soft target data set B *;} (3) learning a student model composed of a student network and a student random forest using the ^{data set B *} generated in step (2); And (4) performing classification by combining the two outputs of the student network and the student random forest learned in step (3). According to the teacher-student framework for weight reduction of an ensemble classifier combined with a deep network and a random forest proposed in the present invention and a classification method based thereon, a new ensemble classifier is developed by combining a deep network and a random forest, ^{By training the student model using the soft target data set B *} , which is the output of the teacher model, the ensemble classifier developed through the teacher-student framework consisting of the teacher model and the student model is lightened and outputs more flexible classification results. The teacher model can be trained using the data set A including the class label and the data set B not including the class label, thereby preventing overfitting of the teacher model.