A Reverse Nearest Neighbor Based Instance Selection Algorithm

• Main Authors: Shu-ming Hsu, 許書銘
• Other Authors: Bi-ru Dai
• Format: Others
• Language: en_US
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/67336232094961102183

碩士 === 國立臺灣科技大學 === 資訊工程系 === 99 === Data reduction is to extract a subset from a dataset. The advantage of data reduction is decreasing the requirement of storage. Using the subset as training data is possible to maintain classification accuracy; sometimes, it can be further improved because of eliminating noises. The key is how to choose representative samples while ignoring noises at the same time. Many instance selection algorithms are based on Nearest Neighbor decision rule (NN). Some of these algorithms select samples based on two strategies, incremental and decremental. The first type of algorithms selects some instances as samples and iteratively adds instances which do not have the same class label with their nearest sample to the sample set. The second type of algorithms gradually removes instances based on its own strategies. However, we propose an algorithm based on Reverse Nearest Neighbor (RNN), called Reverse Nearest Neighbor Reduction (RNNR). RNNR selects samples which can represent other instances in the same class. In addition, RNNR does not need to iteratively scan a dataset which takes much processing time. Experimental results show that RNNR generally achieves higher accuracy, selects fewer samples and takes less processing time than comparators.