干旱建模的数据采掘属性选择方法--以非洲之角为例
Data Mining Attribute Selection Approach for Drought Modeling: A Case Study for Greater Horn of Africa
摘要：
本文的目标是：1）开发一种经验方法，用于选择干旱建模的相关属性; 2）选择与大非洲之角干旱建模和预测最相关的属性。来自不同领域的24个属性用于此实验分析。两个属性选择算法用于本研究：主成分分析（PCA）和基于相关性的属性选择（CAS）。使用PCA和CAS算法，24个属性的排序，他们的价值值。因此，选择了15个属性来模拟GHA的干旱。所选属性的平均价值从0.5到0.9不等。未来的研究可能会使用相关的分类技术来评估所开发的方法，并量化从所开发的方法中获得的实际信息。
英文摘要：
The objectives of this paper were to 1) develop an empirical method for selecting relevant attributes for modelling drought, and 2) select the most relevant attribute for drought modelling and predictions in the Greater Horn of Africa (GHA). Twenty four attributes from different domain areas were used for this experimental analysis. Two attribute selection algorithms were used for the current study: Principal Component Analysis (PCA) and correlation-based attribute selection (CAS). Using the PCA and CAS algorithms, the 24 attributes were ranked by their merit value. Accordingly, 15 attributes were selected for modelling drought in GHA. The average merit values for the selected attributes ranged from 0.5 to 0.9. Future research may evaluate the developed methodology using relevant classification techniques and quantify the actual information gain from the developed approach.
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