A meta-learning network method for few-shot multi-class classification problems with numerical data

Abstract

The support vector machine (SVM) method is an important basis of the current popular multi-class classification (MCC) methods and requires a sufficient number of samples. In the case of a limited number of samples, the problem of over-learning easily occurs, resulting in unsatisfactory classification. Therefore, this work investigates an MCC method that requires only a small number of samples. During model construction, raw data are converted into two-dimensional form via preprocessing. Via feature extraction, the learning network is measured and the loss function minimization principle is considered to better solve the problem of learning based on a small sample. Finally, three examples are provided to illustrate the feasibility and effectiveness of the proposed method.