Cláudio Rebelo de Sá

Mining association rules for label ranking

Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we propose an adaptation of association rules for label ranking. The adaptation, which is illustrated in this work with APRIORI Algorithm, essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. We also adapt the method to make a prediction from the possibly conflicting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, the results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.

Multi-interval Discretization of Continuous Attributes for Label Ranking

Label Ranking (LR) problems, such as predicting rankings of financial analysts, are becoming increasingly important in data mining. While there has been a significant amount of work on the development of learning algorithms for LR in recent years, pre-processing methods for LR are still very scarce. However, some methods, like Naive Bayes for LR and APRIORI-LR, cannot deal with real-valued data directly. As a make-shift solution, one could consider conventional discretization methods used in classification, by simply treating each unique ranking as a separate class. In this paper, we show that such an approach has several disadvantages. As an alternative, we propose an adaptation of an existing method, MDLP, specifically for LR problems. We illustrate the advantages of the new method using synthetic data. Additionally, we present results obtained on several benchmark datasets. The results clearly indicate that the discretization is performing as expected and in some cases improves the results of the learning algorithms.

Exceptional Preferences Mining

Exceptional Preferences Mining (EPM) is a crossover between two subfields of datamining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where the preference relations between subsets of the labels significantly deviate from the norm; a variant of Subgroup Discovery, with rankings as the (complex) target concept. We employ three quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes ‘exceptional’ varies with the quality measure: the first gauges exceptional overall ranking behavior, the second indicates whether a particular label stands out from the rest, and the third highlights subgroups featuring unusual pairwise label ranking behavior. As proof of concept, we explore five datasets. The results confirm that the new task EPM can deliver interesting knowledge. The results also illustrate how the visualization of the preferences in a Preference Matrix can aid in interpreting exceptional preference subgroups.

Preference rules for label ranking: Mining patterns in multi-target relations

In this paper, we investigate two variants of association rules for preference data, Label Ranking Association Rules and Pairwise Association Rules. Label Ranking Association Rules (LRAR) are the equivalent of Class Association Rules (CAR) for the Label Ranking task. In CAR, the consequent is a single class, to which the example is expected to belong to. In LRAR, the consequent is a ranking of the labels. The generation of LRAR requires special support and confidence measures to assess the similarity of rankings. In this work, we carry out a sensitivity analysis of these similarity-based measures. We want to understand which datasets benefit more from such measures and which parameters have more influence in the accuracy of the model. Furthermore, we propose an alternative type of rules, the Pairwise Association Rules (PAR), which are defined as association rules with a set of pairwise preferences in the consequent. While PAR can be used both as descriptive and predictive models, they are essentially descriptive models. Experimental results show the potential of both approaches.

Discovering a taste for the unusual: exceptional models for preference mining

Exceptional preferences mining (EPM) is a crossover between two subfields of data mining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where some preference relations between labels significantly deviate from the norm. It is a variant of subgroup discovery, with rankings of labels as the target concept. We employ several quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes ‘exceptional’ varies with the quality measure: two measures look for exceptional overall ranking behavior, one measure indicates whether a particular label stands out from the rest, and a fourth measure highlights subgroups with unusual pairwise label ranking behavior. We explore a few datasets and compare with existing techniques. The results confirm that the new task EPM can deliver interesting knowledge.

Distance-Based Decision Tree Algorithms for Label Ranking

The problem of Label Ranking is receiving increasing attention from several research communities. The algorithms that have developed/adapted to treat rankings as the target object follow two different approaches: distribution-based (e.g., using Mallows model) or correlation-based (e.g., using Spearman’s rank correlation coefficient). Decision trees have been adapted for label ranking following both approaches. In this paper we evaluate an existing correlation-based approach and propose a new one, Entropy-based Ranking trees. We then compare and discuss the results with a distribution-based approach. The results clearly indicate that both approaches are competitive.