COST, S. and SALZBERG, S. 1993, A weighted nearest neighbor algorithm for learning with symbolic features, Machine Learning, 10, 5778. Home/Search Document Details and Download
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....these points so that, given any query point q X, the data point nearest to q can be reported quickly. Nearest neighbor searching has applications in many areas, including knowledge discovery and data mining [FPSSU96] pattern recognition and classification [CH67, DH73] machine learning [CS93] data compression [GG92] multimedia databases [FSN 95] document retrieval [DDF 90] and statistics [DW82] There are many possible choices of the metric space. Throughout we will assume that the space is R , real d dimensional space, where distances are measured using any Minkowski ....
S. Cost and S. Salzberg, A weighted nearest neighbor algorithm for learning with symbolic features, Machine Learning 10 (1993), 57--78.
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....pair of symbolic values across all classes. Two feature values have a small distance if their relative frequencies are approximately equal for all output classes. Cost and Salzberg present a nearest neighbor algorithm that uses a modification of VDM, called MVDM (Modified Value Difference Metric) [15]. The main difference between MVDM and VDM is that their method s feature value differences are symmetric. This is not the case for VDM. A comparison of MVDM and Bayesian classifier is presented in [56] A generalization of the nearest neighbor algorithm, k NN, classifies a new instance by a ....
S. Cost, S. Salzberg, A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features, Machine Learning, 10(1):57-58, 1993.
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.... decision trees [55] have been the subject of much recent experimental work, the nearest neighbor algorithms continues to stay as an accurate learning technique [64] The nearest neighbor learning algorithms have been shown to work as well as other machine learning methods despite their simplicity [16, 18, 68]. It seems that nearest neighbor methods will continue to be cited as a basis of comparison with other methods. The NN classification algorithm is based on the assumption that examples which are closer in the instance space are of the same class. That is, unclassified ones should belong to the ....
....of distinct values across all classes. Two feature values have a small distance if their relative frequencies are approximately equal for all output classes. Cost and Salzberg presented a nearest neighbor algorithm that uses a modification of VDM, called MVDM (Modified Value Difference Metric) [16]. The main difference between MVDM and VDM is that their method s feature value differences are symmetric. This is not the case for VDM. A comparison of MVDM and Bayesian classifier is presented in [59] NN algorithm can be quite effective when the features of the domain are equally important. ....
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S. Cost, S. Salzberg, A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features, Machine Learning, 10(1):57--58, 1993.
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COST, S. and SALZBERG, S. 1993, A weighted nearest neighbor algorithm for learning with symbolic features, Machine Learning, 10, 5778.
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S. Cost and S. Salzberg. A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning, 10:57--78, 1993.
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