@Override public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.ARTICLE); result.setValue(Field.AUTHOR, "Jesse Read, Luca Martino, David Luengo, Pablo Olmos"); result.setValue(Field.TITLE, "Scalable multi-output label prediction: From classifier chains to classifier trellises"); result.setValue(Field.JOURNAL, "Pattern Recognition"); result.setValue(Field.URL, "http://www.sciencedirect.com/science/article/pii/S0031320315000084"); result.setValue(Field.YEAR, "2015"); return result; }
@Override public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.ARTICLE); result.setValue(Field.AUTHOR, "Yuhong Guoand and Suicheng Gu"); result.setValue(Field.TITLE, "Multi-Label Classification Using Conditional Dependency Networks"); result.setValue(Field.BOOKTITLE, "IJCAI '11"); result.setValue(Field.YEAR, "2011"); result.add(new CT().getTechnicalInformation()); return result; }
/** * Adds an empty technical information with the given type to the list of * additional informations and returns the instance. * * @param type the type of the new information to add * @return the generated information */ public TechnicalInformation add(Type type) { TechnicalInformation result; result = new TechnicalInformation(type); add(result); return result; }
/** * Returns a string describing this data generator. * * @return a description of the data generator suitable for displaying in the * explorer/experimenter gui */ public String globalInfo() { return "Generates a people database and is based on the paper by Agrawal " + "et al.:\n" + getTechnicalInformation().toString(); }
/** * Returns a string describing the stemmer * @return a description suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "A stemmer based on the Lovins stemmer, described here:\n\n" + getTechnicalInformation().toString(); }
@Override public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "J\"org Wicker, Bernhard Pfahringer, Stefan Kramer"); result.setValue(Field.TITLE, "Multi-Label Classification using Boolean Matrix Decomposition"); result.setValue(Field.BOOKTITLE, "Proceedings of the 27th Annual ACM Symposium on Applied Computing"); result.setValue(Field.YEAR, "2012"); result.setValue(Field.PAGES, "179-186"); return result; }
/** * This will return a string describing the classifier. * * @return The string. */ @Override public String globalInfo() { return "This Bayes Network learning algorithm determines the maximum weight spanning tree " + " and returns a Naive Bayes network augmented with a tree.\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); } // globalInfo
@Override public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.ARTICLE); result.setValue(Field.AUTHOR, "Yuhong Guoand and Suicheng Gu"); result.setValue(Field.TITLE, "Multi-Label Classification Using Conditional Dependency Networks"); result.setValue(Field.BOOKTITLE, "IJCAI '11"); result.setValue(Field.YEAR, "2011"); result.add(new CT().getTechnicalInformation()); return result; }
@Override public TechnicalInformation getTechnicalInformation() { TechnicalInformation technicalInfo = new TechnicalInformation(Type.ARTICLE); technicalInfo.setValue(Field.AUTHOR, "Koby Crammer, Yoram Singer"); technicalInfo.setValue(Field.YEAR, "2003"); technicalInfo.setValue(Field.TITLE, "A Family of Additive Online Algorithms for Category Ranking."); technicalInfo.setValue(Field.JOURNAL, "Journal of Machine Learning Research"); technicalInfo.setValue(Field.VOLUME, "3(6)"); technicalInfo.setValue(Field.PAGES, "1025-1058"); return technicalInfo; }
/** * Returns a string describing classifier * * @return a description suitable for displaying in the explorer/experimenter * gui */ public String globalInfo() { return "Classifier for building 'logistic model trees', which are classification trees with " + "logistic regression functions at the leaves. The algorithm can deal with binary and multi-class " + "target variables, numeric and nominal attributes and missing values.\n\n" + "For more information see: \n\n" + getTechnicalInformation().toString(); }
@Override public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "Jesse Read"); result.setValue(Field.TITLE, "A Pruned Problem Transformation Method for Multi-label Classification"); result.setValue(Field.BOOKTITLE, "NZ Computer Science Research Student Conference. Christchurch, New Zealand"); result.setValue(Field.YEAR, "2008"); return result; }
/** * Returns a string describing this object * @return a description of the classifier suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Class for performing a Bias-Variance decomposition on any classifier " + "using the method specified in:\n\n" + getTechnicalInformation().toString(); }
@Override public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "Julio H. Zaragoza et al."); result.setValue(Field.TITLE, "Bayesian Chain Classifiers for Multidimensional Classification"); result.setValue(Field.BOOKTITLE, "IJCAI'11: International Joint Conference on Artificial Intelligence."); result.setValue(Field.YEAR, "2011"); return result; }
/** * Returns a string describing classifier. * @return a description suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "K-nearest neighbours classifier. Can " + "select appropriate value of K based on cross-validation. Can also do " + "distance weighting.\n\n" + "For more information, see\n\n" + getTechnicalInformation().toString(); }
@Override public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "Jesse Read and Jaako Hollmen"); result.setValue(Field.TITLE, "A Deep Interpretation of Classifier Chains"); result.setValue(Field.BOOKTITLE, "Advances in Intelligent Data Analysis {XIII} - 13th International Symposium, {IDA} 2014"); result.setValue(Field.PAGES, "251--262"); result.setValue(Field.YEAR, "2014"); return result; }
/** * Returns a string describing the kernel * * @return a description suitable for displaying in the explorer/experimenter * gui */ @Override public String globalInfo() { return "The Pearson VII function-based universal kernel.\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); }
@Override public TechnicalInformation getTechnicalInformation() { TechnicalInformation result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "Read, Jesse"); result.setValue(Field.TITLE, "A Pruned Problem Transformation Method for Multi-label classification"); result.setValue(Field.PAGES, "143-150"); result.setValue(Field.BOOKTITLE, "Proc. 2008 New Zealand Computer Science Research Student Conference (NZCSRS 2008)"); result.setValue(Field.YEAR, "2008"); return result; }
/** * Returns a string describing classifier * @return a description suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Class for constructing a balanced forest of random trees.\n\n" + "For more information see: \n\n" + getTechnicalInformation().toString(); }
@Override public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "Weiwei Cheng and Krzysztof Dembczyn and Eyke Hullermeier"); result.setValue(Field.TITLE, "Bayes Optimal Multi-label Classification via Probabalistic Classifier Chains"); result.setValue(Field.BOOKTITLE, "ICML '10: 27th International Conference on Machine Learning"); result.setValue(Field.YEAR, "2010"); return result; }
/** * Description to display in the GUI. * * @return the description */ @Override public String globalInfo() { return "Maps the output of a multi-label classifier to a known label combination using the hamming distance." + "For more information see:\n" + getTechnicalInformation().toString(); }