sourcecode+API

mulan.classifier.lazy Class java.lang.Object
 * 1) ===BRkNN===

mulan.classifier.MultiLabelLearnerBase mulan.classifier.lazy.MultiLabelKNN mulan.classifier.lazy.BRkNN All Implemented Interfaces: Serializable, MultiLabelLearner, TechnicalInformationHandler

public class BRkNN extends MultiLabelKNN Simple BR implementation of the KNN algorithm Class implementing the base BRkNN algorithm and its 2 extensions BRkNN-a and BRkNN-b. 这个类实现了BRkNN算法和它的两个扩展算法-a和-b For more information: E. Spyromitros, G. Tsoumakas, I. Vlahavas, An Empirical Study of Lazy Multilabel Classification Algorithms, Proc. 5th Hellenic Conference on Artificial Intelligence (SETN 2008), Springer, Syros, Greece, 2008. http://mlkd.csd.auth.gr/multilabel.html

BibTeX: @inproceedings{1428385, author = {Spyromitros, Eleftherios and Tsoumakas, Grigorios and Vlahavas, Ioannis}, title = {An Empirical Study of Lazy Multilabel Classification Algorithms}, booktitle = {SETN '08: Proceedings of the 5th Hellenic conference on Artificial Intelligence}, year = {2008}, isbn = {978-3-540-87880-3}, pages = {401--406}, doi = {http://dx.doi.org/10.1007/978-3-540-87881-0_40}, publisher = {Springer-Verlag}, address = {Berlin, Heidelberg}, } 上面都不重要，，看看学英语

Author: Eleftherios Spyromitros-Xioufis ( espyromi@csd.auth.gr ) See Also: Serialized Form

Nested Class Summary static class BRkNN.ExtensionType The two types of extensions

Field Summary

Fields inherited from class mulan.classifier.lazy.MultiLabelKNN

dfunc, distanceWeighting, dontNormalize, lnn, numOfNeighbors, train, WEIGHT_INVERSE, WEIGHT_NONE, WEIGHT_SIMILARITY

Fields inherited from class mulan.classifier.MultiLabelLearnerBase

featureIndices, labelIndices, numLabels

Constructor Summary BRkNN(int numOfNeighbors) The default constructor //默认构造函数，包含最近邻数目// //BRkNN(int numOfNeighbors, BRkNN.ExtensionType ext)// //Constructor giving the option to select an extension of the base version// 选择扩展算法类型

Method Summary protected void buildInternal(MultiLabelInstances aTrain) Learner specific implementation of building the model from MultiLabelInstances training data set. 从训练数据中，得到分类模型 protected void crossValidate Select the best value for k by hold-one-out cross-validation. //k折交叉验证，优选最好结果// //TechnicalInformation// //getTechnicalInformation// //Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.// 得到技术信息 protected boolean[] labelsFromConfidences2(double[] confidences) used for BRknn-a // protected boolean[] labelsFromConfidences3(double[] confidences) used for BRkNN-b (break ties arbitrarily) protected MultiLabelOutput makePredictionInternal(Instance instance) weka Ibk style prediction void setCvMaxK(int cvMaxK) set the maximum number of neighbors to be evaluated via cross-validation void setkSelectionViaCV(boolean flag)