Semi-supervised learning (SSL) is a common approach to learning predictive models using not only labeled, but also unlabeled examples. While SSL for the simple tasks of classification and regression has received much attention from the research community, this is not the case for complex prediction tasks with structurally dependent variables, such as multi-label classification and hierarchical multi-label classification. These tasks may require additional information, possibly coming from the underlying distribution in the descriptive space provided by unlabeled examples, to better face the challenging task of simultaneously predicting multiple class labels. In this paper, we investigate this aspect and propose a (hierarchical) multi-label classification method based on semi-supervised learning of predictive clustering trees, which we also extend towards ensemble learning. Extensive experimental evaluation conducted on 24 datasets shows significant advantages of the proposed method and its extension with respect to their supervised counterparts. Moreover, the method preserves interpretability of classical tree-based models.

Meta learning generalizes the empirical experience with different learning tasks and holds promise for providing important empirical insight into the behavior of machine learning algorithms.In this paper, we present a comprehensive meta-learning study of data sets and methods for multilabel classification (MLC).MLC is a practically relevant machine learning task where each example is labeled with multiple labels simultaneously.Here, we analyze 40 MLC data sets by using 50 meta features describing different properties of the data.The main findings of this study are as follows.First, the most prominent meta features that describe the space of MLC data sets are the ones assessing different aspects of the label space.Second, the meta models show that the most important meta features describe the label space, and, the meta features describing the relationships among the labels tend to occur a bit more often than the meta features describing the distributions between and within the individual labels.Third, the optimization of the hyperparameters can improve the predictive performance, however, quite often the extent of the

In this study, we propose two novel (groups of) methods for unsupervised feature ranking and selection. The first group includes feature ranking scores (Genie3 score, RandomForest score) that are computed from ensembles of predictive clustering trees. The second method is URelief, the unsupervised extension of the Relief family of feature ranking algorithms. Using 26 benchmark data sets and 5 baselines, we show that both the Genie3 score (computed from the ensemble of extra trees) and the URelief method outperform the existing methods and that Genie3 performs best overall, in terms of predictive power of the top-ranked features. Additionally, we analyze the influence of the hyper-parameters of the proposed methods on their performance and show that for the Genie3 score the highest quality is achieved by the most efficient parameter configuration. Finally, we propose a way of discovering the location of the features in the ranking, which are the most relevant in reality.

Recommender systems use big data methods, and are widely used in various social-network, e-commerce, and content platforms. With their increased relevance, online platforms and developers are in need of better ways to choose the systems that are most suitable for their use-cases. At the same time, the research literature on recommender systems describes a multitude of measures to evaluate the performance of different algorithms. For the end-user however, the large number of available measures do not provide much help in deciding which algorithm to deploy. Some of the measures are correlated, while others deal with different aspects of recommendation performance like accuracy and coverage. To address this problem, we propose a novel benchmarking framework that mixes different evaluation measures in order to rank the recommender systems on each benchmark dataset, separately. Additionally, our approach discovers sets of correlated measures as well as sets of evaluation measures that are least correlated. We investigate the robustness of the proposed methodology using published results from an experimental study involving multiple big datasets and evaluation measures. Our work provides a general framework that can handle an arbitrary number of evaluation measures and help end-users rank the systems available to them.