Gradient boosting method has been successfully used for single target prediction problems. In real world applications, however, problems involving the prediction of multiple target attributes are often of interest. In this paper, a multi-target boosting method for regression problems, named as MTBR, is proposed. Although MTBR builds one model for each target attribute separately, all the target attributes are utilized when building each model. In each boosting iteration, the base learner, the regression tree in particular, is learned by selecting the best models from all the target attributes. We also introduce a novel knowledge transfer approach. That is, the tree structure learned from one target attribute, representing a way to partition the feature space, is used to predict another target attribute. Experiments with six data sets compare MTBR to other ensemble regression methods, and prove the effectiveness of MTBR in leveraging the knowledge of multiple target attributes and improving the model accuracy.

Black-box machine translation systems have proven incredibly useful for a variety of applications yet by design are hard to adapt, tune to a specific domain, or build on top of. In this work, we introduce a method to improve such systems via automatic pre-processing (APP) using sentence simplification. We first propose a method to automatically generate a large in-domain paraphrase corpus through back-translation with a black-box MT system, which is used to train a paraphrase model that “simplifies” the original sentence to be more conducive for translation. The model is used to preprocess source sentences of multiple low-resource language pairs. We show that this preprocessing leads to better translation performance as compared to non-preprocessed source sentences. We further perform side-by-side human evaluation to verify that translations of the simplified sentences are better than the original ones. Finally, we provide some guidance on recommended language pairs for generating the simplification model corpora by investigating the relationship between ease of translation of a language pair (as measured by BLEU) and quality of the resulting simplification model from back-translations of this language pair (as measured by SARI), and tie this into the downstream task of low-resource translation.

Clustering is an important aspect of data mining, while clustering high-dimensional mixed-attribute data in a scalable fashion still remains a challenging problem. In this paper, we propose a tree-ensemble clustering algorithm for static datasets, CRAFTER, to tackle this problem. CRAFTER is able to handle categorical and numeric attributes simultaneously, and scales well with the dimensionality and the size of datasets. CRAFTER leverages the advantages of a tree-ensemble to handle mixed attributes and high dimensionality. The concept of the class probability estimates is utilized to identify the representative data points for clustering. Through a series of experiments on both synthetic and real datasets, we have demonstrated that CRAFTER is superior than Random Forest Clustering (RFC), an existing tree-based clustering method, in terms of both the clustering quality and the computational cost.