Mutually Guided Dendritic Neural Models

With the explosive growth of Internet data, it has become quite easy to collect unlabeled training data in many practical machine learning and its applications, but it is relatively difficult to obtain a large amount of labeled training data. Meanwhile, based on the idea of semi-supervised learning, we propose the mutually guided dendritic neural models (MGDNM) framework, which can realize the expansion of labeled datasets. MGDNM utilizes two base classifiers for data exchange, so as to achieving complementary advantages. To simulate the problem of insufficient labeled data, we used 20% of the dataset as the training dataset. On this basis, we conducted experiments on three datasets (Iris, Breast Cancer and Glass). By calculating the accuracy and confusion matrices, the comparison shows that the classification effect of MGDNM is significantly higher than dendritic neural model (DNM), Support Vector Machine (SVM), Gaussian Naive Bayesian (GaussianNB) and Back Propagation Neural Network (BP). It shows that MGDNM framework is effective and feasible.