A Generalized k-Nearest Neighbor Rule
A Generalized k-Nearest Neighbor Rule
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DOI：
10.1016/s0019-9958(70)90081-1
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发表时间：
1970-04
期刊：
影响因子：
--
通讯作者：
E. Patrick;F. P. Fischer
中科院分区：
文献类型：
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作者：
E. Patrick;F. P. Fischer
描述了一类基于容差区域的有监督的非参数决策规则，其中包括两类情况下的k近邻决策规则。这样做有两个实际原因：第一，可以指定一族类似于k-近邻规则的决策规则，它适用于更广泛的模式识别问题。这是因为在一般的规则类中，每个训练样本集所需的训练样本数和各自的先验类概率之间的约束被削弱；并且，可以引入离散损失函数来加权决策错误的有限种方法的重要性。其次，在基于容差区域的决策规则族中，决策规则具有允许对训练集数据进行预处理的性质，从而导致显著的数据约简。
A family of supervised, nonparametric decision rules, based on tolerance regions, is described which includes thek-Nearest Neighbor decision rules when there are two classes. There are two practical reasons for doing so: first, a family of decision rules similar to thek-Nearest Neighbor rules can be specified which applies to a broader collection of pattern recognition problems. This is because in the general class of rules constraints are weakened between the number of training samples required in each training sample set and the respective a priori class probabilities; and, a discrete loss function weighting the importance of the finite number of ways to make a decision error can be introduced.Second, within the family of decision rules based on tolerance regions, there are decision rules which have a property allowing for preprocessing of the training set data resulting in significant data reduction.Theoretical performance for a special case is presented.