Abstract Label ranking aims to learn a mapping from instances to rankings over a finite number of predefined labels. Random forest is a powerful and one of the most successful general-purpose machine learning algorithms of modern times. In this paper, we present a powerful random forest label ranking method which uses random decision trees to retrieve nearest neighbors. We have developed a novel two-step rank aggregation strategy to effectively aggregate neighboring rankings discovered by the random forest into a final predicted ranking. Compared with existing methods, the new random forest method has many advantages including its intrinsically scalable tree data structure, highly parallel-able computational architecture and much superior performance. We present extensive experimental results to demonstrate that our new method achieves the highly competitive performance compared with state-of-the-art methods for datasets with complete ranking and datasets with only partial ranking information.
The second category is probabilistic approaches which represent label ranking based on statistical models for ranking data, i.e., parametrized probability distributions on the class of all rankings. For example, have developed instance-based (IB) learning algorithms based on the Mallows (M) and Plackett-Luce (PL) models @cite_9 @cite_6 , and proposed a label ranking method based on Gaussian mixture models @cite_13 .
@cite_9 @cite_13 @cite_6
2107853397 2085247959 1526445072
The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ranking that complement and improve upon existing approaches. More specifically, we propose extensions of two methods that have been used extensively for classification and regression so far, namely instance-based learning and decision tree induction. The unifying element of the two methods is a procedure for locally estimating predictive probability models for label rankings. Label ranking studies the issue of learning a model that maps instances to rankings over a finite set of predefined labels. In order to relieve the cost of memory and time during training and prediction, we propose a novel approach for label ranking problem based on Gaussian mixture model in this paper. The key idea of the approach is to divide the label ranking training data into multiple clusters using clustering algorithm, and each cluster is described by a Gaussian prototype. Then, a Gaussian mixture model is introduced to model the mapping from instances to rankings. Finally, a predicted ranking is obtained with maximum posterior probability. In the experiments, we compare our method with two state-of-the-art label ranking approaches. Experimental results show that our method is fully competitive in terms of predictive accuracy. Moreover, the proposed method also provides a measure of the reliability of the corresponding predicted ranking. This paper introduces two new methods for label ranking based on a probabilistic model of ranking data, called the Plackett-Luce model. The idea of the first method is to use the PL model to fit locally constant probability models in the context of instance-based learning. As opposed to this, the second method estimates a global model in which the PL parameters are represented as functions of the instance. Comparing our methods with previous approaches to label ranking, we find that they offer a number of advantages. Experimentally, we moreover show that they are highly competitive to start-of-the-art methods in terms of predictive accuracy, especially in the case of training data with incomplete ranking information.