The k-nearest neighbor decision rule is one of the simplest decision rules to implement and has...

 

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The k-nearest neighbor decision rule is one of the simplest decision rules to implement and has a good performance in practice. Answer the following questions. (a) Suppose the Bayes error rate for a c = 3 category classification problem is 5%. What are the bounds on the error rate of a nearest-neighbor classifier trained with an "infinitely large” training set? (b) How should we select the value of k? (c) One of the well-known methods to reduce the complexity of the nearest neighbor classifier is called editing, pruning or condensing. (i) Briefly describe this nearest- neighbor editing procedure. (ii) Illustrate the editing procedure with a simple 2-class 2-dimensional data set. The k-nearest neighbor decision rule is one of the simplest decision rules to implement and has a good performance in practice. Answer the following questions. (a) Suppose the Bayes error rate for a c = 3 category classification problem is 5%. What are the bounds on the error rate of a nearest-neighbor classifier trained with an "infinitely large” training set? (b) How should we select the value of k? (c) One of the well-known methods to reduce the complexity of the nearest neighbor classifier is called editing, pruning or condensing. (i) Briefly describe this nearest- neighbor editing procedure. (ii) Illustrate the editing procedure with a simple 2-class 2-dimensional data set.

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a The error rate of a nearestneighbor classifier trained with an infinitely large training set is guaranteed to be within twice the Bayes error rate S... View the full answer