Machine Learning Algorithms for Fostering Innovative Education for University Students

Abstract

Data augmentation with mixup has been proven effective in various machine learning tasks. However, previous methods primarily concentrate on generating previously unseen virtual examples using randomly selected mixed samples, which may overlook the importance of similar spatial distributions. In this work, we extend mixup and propose MbMix, a novel yet simple training approach designed for implementing mixup with memory batch augmentation. MbMix specifically selects the samples to be mixed via memory batch to guarantee that the generated samples have the same spatial distribution as the dataset samples. Conducting extensive experiments, we empirically validate that our method outperforms several mixup methods across a broad spectrum of text classification benchmarks, including sentiment classification, question type classification, and textual entailment. Of note, our proposed method achieves a 5.61% improvement compared to existing approaches on the TREC-fine benchmark. Our approach is versatile, with applications in sentiment analysis, question answering, and fake news detection, offering entrepreneurial teams and students avenues to innovate. It enables simulation and modeling for student ventures, fostering an entrepreneurial campus culture and mindset.