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Hybrid Classification System for Uncertain Data
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DOI：10.1109/TSMC.2016.2622247.png)
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Abstract
En 中文
In classification problem, several different classes may be partially overlapped in their borders. The objects in the border are usually quite difficult to classify. A hybrid classification system (HCS) is proposed to adaptively utilize the proper classification method for each object according to the K-nearest neighbors (K-NNs), which are found in the weighting vector space obtained by self-organizing map (SOM) in each class. If the K-close weighting vectors (nodes) are all from the same class, it indicates that this object can be correctly classified with high confidence, and the simple hard classification will be adopted to directly classify this object into the corresponding class. If the object likely lies in the border of classes, it implies that this object could be difficult to classify, and the credal classification working with belief functions is recommended. The credal classification allows the object to belong to both singleton classes and sets of classes (meta-class) with different masses of belief, and it is able to well capture the potential imprecision of classification thanks to the meta-class and also reduce the errors. Fuzzy classification is selected for the object close to the border and hard to clearly classify, and it associates the object with different classes by different membership (probability) values. HCS generally takes full advantage of the three classification ways and produces good performance. Moreover, it requires quite low computational burden compared with other K-NNs-based methods due to the use of SOM. The effectiveness of HCS is demonstrated by several experiments with synthetic and real datasets.
Keywords:
Belief function
Dempster-Shafer theory (DST)
evidence theory
pattern classification
uncertain data
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