Hybrid Machine Learning of Data Prediction for Applications

• Main Authors: Jhih-Ying Lian, 連芷濙
• Other Authors: Chunshien Li
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/wd5suf

碩士 === 國立中央大學 === 資訊管理學系 === 106 === In the study, the predictive model is a complex fuzzy neural model. The complex fuzzy sets are used to replace the traditional fuzzy set used in the traditional fuzzy neural network. Based on parallel operation with the particle swarm optimization (PSO) algorithm and the random optimization (RO) algorithm, an improved algorithm is proposed, and combined with the recursive least squares estimation (RLSE) into a hybrid machine learning algorithm, called the RoPso-RLSE learning method. In addition, a feature selection method based on Shannon entropy is presented to select useful features which will be used as model inputs in modeling. In this study, the feature selection, complex neural fuzzy system and hybrid machine learning algorithm are used for time series prediction of stock price and exchange rate. The feature selection selects features by calculating the information provided by the features for the targets. Complex fuzzy sets (CFSs) have better description for set-element relationship than tradition fuzzy sets in membership. They can be used in neural fuzzy networks to transmit more information and increasing the prediction performance of model. Moreover, due to the property of CFSs, the model can perform multi-target forecasting simultaneously. In the machine learning stage, the hybrid algorithm RoPso, compared to use single PSO or RO only, can increase the probability of finding the optimal solution, with fast learning convergence. In addition, combining the RLSE with RoPso can reduce the loading of machine learning by the RoPso alone. Several real-world data sets of stock prices and exchange rates have been used to test the proposed approach in the experiments for multi-objective prediction. Through the experimental results, the proposed approach has shown good performance.