Wei-Yuan Dzan

A CBR-based fuzzy decision tree approach for database classification

Database classification suffers from two well-known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case-based reasoning technique, a fuzzy decision tree (FDT), and genetic algorithms (GAs) to construct a decision-making system for data classification in various database applications. The model is major based on the idea that the historic database can be transformed into a smaller case base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller case-based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated experimentally compared with other approaches on different database classification applications. The average hit rate of our proposed model is the highest among others.

Forecasting of manufacturing cost in mobile phone products by case-based reasoning and artificial neural network models

The mobile phone manufacturers in Taiwan have made great efforts in proposing the rational quotations to the international phone companies with the ambition to win the bids by out beating other phone manufacturers. However, there are a lot of uncertainties and issues to be resolved in estimating the manufacturing costs for mobile phone manufacturers. As far as we know, there is no existing model which can be applied directly in forecasting the manufacturing costs. This research makes the first attempt to develop a hybrid system by integrated Case-Based Reasoning (CBR) and Artificial Neural Networks (ANN) as a Product Unit Cost (PUC) forecasting model for Mobile Phone Company. According to the cost formula of the mobile phone and experts’ opinions, a set of qualitative and quantitative factors are analyzed and determined. Qualitative factors are applied in CBR to retrieve a similar case from the case bases for a new phone product and ANN is used to find the relationship between the quantitative factors and the predicted PUC. Finally, intensive experiments are conducted to test the effectiveness of six different forecasting models. The model proposed in this research is compared with the other five models and the MAPE value of the proposed model is the smallest. This research provides a new prediction model with high accuracy for mobile phone manufacturing companies.

Construction and Application of Incorporating Imagination Cooperative Learning with Team Game Tournament

With the rapid advancement of information technology and the Internet, along with the saturation, development, and transcendence of time and space of information technology, the unavoidable trend of educational reform needs to consider how to adequately engage in instructional design, reconstruction of traditional instruction and internet education, develop new learning forms and models, develop new curricular content, and create new instructional methods in order to create new forms of education. The purpose of school education bears the burden of cultivating various engineering and technical personnel, and this is a key factor for overall national construction and development. Thus, if students can enhance imagination instruction, and engage in technological practice involving their imagination, plan the optimal imagination cooperative instructional model to effectively elevate student imagination innovative and research and development ability, this would not only promote the development and advancement of technological courses, but can serve as a model and exemplar for other subjects. This would result in the overall elevation of national education.