基於軟式計算高效能預測模式之發展與應用

• Main Authors: Hung Che Lo, 羅宏哲
• Other Authors: 陳善泰
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/18892426311416435215

碩士 === 國防大學理工學院 === 資訊科學碩士班 === 97 === In recent years, data mining techniques have become a popular research topic. With rapid advances in data collection and storage technology, the new challenge is how to discover knowledge from huge data sets. Soft computing is tolerant of imprecision, uncertainty, partial truth, and approximation and is used to cope with complicated problems. In this paper, we take advantages of various soft computing techniques, fuzzy systems, neural networks, and evolutionary computations, to construct an ensemble classification model, the Enhanced Adaptive Hierarchical Classifier (EAHC). The EAHC takes advantage of fuzzy systems to gain extra data attributes and hence is able to construct classification models with higher classification accuracy. The EAHC further integrates several data mining technologies, such as, decision tree, self-organizing map, and fuzzy if-then rules which are used to build basic classifiers. The integration leads to EAHC achieving a better classification results. We have applied the models to solve a critical, real-world problem, namely rainfall intensity classification. EAHC can achieve various goals through setting different fitness functions. Experimental results show that the proposed model is able to achieve high accuracy for rainfall intensity retrieval and outperforms previously published methods. Finally, we apply EAHC to a real typhoon case, which demonstrates relatively high agreement between GPROF and our algorithm.