Roughly Balanced Bagging is one of the most efficient ensembles specialized for class imbalanced data. In this paper, we study its basic properties that may influence its good classification performance. We experimentally analyze them with respect to bootstrap construction, deciding on the number of component classifiers, their diversity, and ability to deal with the most difficult types of the minority examples. Then, we introduce two generalizations of this ensemble for dealing with a higher number of attributes and for adapting it to handle multiple minority classes. Experiments with synthetic and real life data confirm usefulness of both proposals.

Marynowicz, J, Kikut, K, Lango, M, Horna, D, and Andrzejewski, M. Relationship between the session-RPE and external measures of training load in youth soccer training. J Strength Cond Res 34(10): 2800–2804, 2020—The aim of this study was to identify the external training load (TL) markers (10 Hz Global Positioning System) that are most influential on the rating of perceived exertion (RPE) and session-RPE (sRPE) during youth soccer training. Data were collected from 18 youth soccer players during an 18-week in-season period. A total of 804 training observations were undertaken. We observed moderate to very large within-individual correlations between sRPE and measures of external load (r ranging from 0.36 to 0.76). Large, positive within-individual correlations were found between total covered distance, PlayerLoad, number of accelerations, and sRPE (r = 0.70, 0.64, and 0.62, respectively, p < 0.001). By contrast, small to moderate within-individual correlations were noted between RPE and measures of intensity (r ranging from 0.16 to 0.39). A moderate within-individual correlation was observed between high-speed running distance (HSR) per minute and RPE (r = 0.39, p < 0.001). The level of statistical significance was set at alpha = 0.05 for all tests. Two generalized estimating equation models were constructed, with RPE and sRPE as the response variables. The model identified by QIC for RPE contained 2 variables as follows: HSR per minute and distance in deceleration per minute, whereas sRPE was modeled with 3 predictors as follows: PlayerLoad, HSR, and distance in acceleration. The findings demonstrate that RPE does not reflect the intensity of a training session and that sRPE can be a useful, simple, and cost-effective tool for monitoring TL.

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This paper describes a classification system that participated in SemEval-2016 Task 4: Sentiment Analysis in Twitter. The proposed approach competed in subtasks A, B, and C, which involved tweet polarity classification, tweet classification according to a two-point scale, and tweet classification according to a five-point scale. Our system is based on an ensemble consisting of Random Forests, SVMs, and Gradient Boosting Trees, and involves the use of a wide range of features including: ngrams, Brown clustering, sentiment lexicons, Wordnet, and part-of-speech tagging. The proposed system achieved 14 th , 6 th , and 3 rd place in subtasks A, B, and C, respectively.

Sentiment classification is an important task which gained extensive attention both in academia and in industry. Many issues related to this task such as handling of negation or of sarcastic utterances were analyzed and accordingly addressed in previous works. However, the issue of class imbalance which often compromises the prediction capabilities of learning algorithms was scarcely studied. In this work, we aim to bridge the gap between imbalanced learning and sentiment analysis. An experimental study including twelve imbalanced learning preprocessing methods, four feature representations, and a dozen of datasets, is carried out in order to analyze the usefulness of imbalanced learning methods for sentiment classification. Moreover, the data difficulty factors — commonly studied in imbalanced learning — are investigated on sentiment corpora to evaluate the impact of class imbalance.

The relations between multiple imbalanced classes can be handled with a specialized approach which evaluates types of examples’ difficulty based on an analysis of the class distribution in the examples’ neighborhood, additionally exploiting information about the similarity of neighboring classes. In this paper, we demonstrate that such an approach can be implemented as a data preprocessing technique and that it can improve the performance of various classifiers on multiclass imbalanced datasets. It has led us to the introduction of a new resampling algorithm, called Similarity Oversampling and Undersampling Preprocessing (SOUP), which resamples examples according to their difficulty. Its experimental evaluation on real and artificial datasets has shown that it is competitive with the most popular decomposition ensembles and better than specialized preprocessing techniques for multi-imbalanced problems.

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