Browsing by Author "Li, Jung-Bin"

Now showing 1 - 2 of 2

Dynamic fine-tuning stacked auto-encoder neural network for weather forecast
(2018-12-01)
Lin, Szu-Yin
;
Chiang, Chi-Chun
;
Li, Jung-Bin
;
Hung, Zih-Siang
;
Chao, Kuo-Ming
With the advent of the big data era, dynamic and real-time data have increased in both volume and variety. It is difficult to make accurate predictions regarding data as they undergo rapid and dynamic changes. Autonomous cloud computing aims to reduce the time required for traditional machine learning. The stacked auto-encoder is a neural network approach in machine learning for feature extraction. It attempts to model high-level abstractions and to reduce data dimensions by using multiple processing layers. However, some common issues may occur during the implementation of deep learning or neural network models, such as over-complicated dimensions of the input data and difficulty in processing dynamic data. Therefore, combining the concept of dynamic data-driven system with a stacked auto-encoder neural network will help obtain the dynamic data correlation or relationship between the prediction results and actual data in a dynamic environment. This study applies the concept of a dynamic data-driven system to obtain the correlations between the prediction goals and number of different combination results. Association analysis, sequence analysis, and stacked auto-encoder neural network are employed to design a dynamic data-driven system based on deep learning. (C) 2018 Elsevier B.V. All rights reserved.
Refined group learning based on XCS and neural network in intelligent financial decision support system
(2006)
Li, Jung-Bin
;
Chen, An-Pin
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Cooperative learning is widely defined as a process through which a group of individuals interact to achieve a learning goal. In the fluctuating stock market, investors often have various decision-making approaches. This study attempts to exploit computer technology, financial mathematics, and econometrics to make reasonable investment decisions to reduce man-made errors or mistakes and increase profits. This work integrates the eXtended Classifier System (XCS) and neural network modules and incorporates features such as dynamic learning and group decision making. An empirical study is conducted by comparing the profitability of the proposed system with that of investment strategies based on simple rules with single technical indices, individual learning XCS, buy and hold, and six-year term deposit based on the Taiwan Index. The proposed system demonstrates superior performance in terms of accuracy, rate of cumulative return, and variance of return.