Journal of advances in information science and technology

Volume 2, Issue 1, January 2024

1. Research on Interpretable Machine Learning Portfolio Based on Multi-factor Clustering
JiHui Shi, WenZheng Zhang
Pages: 1 - 10

The 'black box' phenomenon and limited interpretability present significant obstacles in machine learning and deep learning for portfolio management. Additionally, standard metrics for interpretability in machine learning often struggle to effectively elucidate model features in portfolio decision contexts. This research aims to address these challenges by introducing a methodology for generating easily interpretable portfolios. The approach involves using Random Forest feature importance analysis within multi-factor models, followed by clustering based on stock factors. Portfolios are generated using the Mean-CVaR model, and the effectiveness of the proposed explainable portfolios is evaluated through comparative analysis with two machine learning interpretability tools: SHAP and Permutation methods.

2. Bach Style Music Authoring System based on Deep Learning
Minghe Kong, Lican Huang
Pages: 11 - 15

With the continuous improvement in various aspects in the field of artificial intelligence, the momentum of artificial intelligence with deep learning for the field of music is coming. The purpose of the research in this paper is to design a Bach style music authoring system based on deep learning. We use a LSTM neural network to train serialized and standardized music feature data. By repeated experiments, we find the optimal LSTM model which can generate imitation of Bach music. Finally the generated music is comprehensively evaluated with the form of online audition and Turing test. The repertoires which the music generation system constructed in this article are very close to the style of Bach’s original music, and it is relatively difficult for ordinary people to distinguish the musics Bach authored from ones AI created.