Class imbalance problem has been a challenging research problem in the fields of machine learning and data mining as most real life datasets are imbalanced. Several existing machine learning algorithms try to maximize the accuracy classification by correctly identifying majority class samples while ignoring the minority class. However, the concept of the minority class instances usually represents a higher interest than the majority class. Recently, several cost sensitive methods, ensemble models and sampling techniques have been used in literature in order to classify imbalance datasets. In this paper, we propose MEBoost, a new boosting algorithm for imbalanced datasets. MEBoost mixes two different weak learners with boosting to improve the performance on imbalanced datasets. MEBoost is an alternative to the existing techniques such as SMOTEBoost, RUSBoost, Adaboost, etc. The performance of MEBoost has been evaluated on 12 benchmark imbalanced datasets with state of the art ensemble methods like SMOTEBoost, RUSBoost, Easy Ensemble, EUSBoost, DataBoost. Experimental results show significant improvement over the other methods and it can be concluded that MEBoost is an effective and promising algorithm to deal with imbalance datasets. The python version of the code is available here: this https URL
DataBoost algorithm or DataBoost-IM method was presented by Hongyu Guo @cite_20 . He proposed an ensemble model which uses data generation. In this algorithm, hard majority and minority class instances are identified during the execution of boosting. Then those hard examples are chosen separately and used to create synthetic instances of respective class. After that those created instances are added to the main dataset. Easy Ensemble is an ensemble method was proposed by Xu-Yung Liu @cite_26 . They create several subsets of majority class instances. Then using each of those datasets it trains a learner. These subsets are created using random under sampling. However, it creates several sub datasets to overcome the main limitation of random under sampling which is it discard instances from majority class randomly regardless of its importance.
@cite_26 @cite_20
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Learning from imbalanced data sets, where the number of examples of one (majority) class is much higher than the others, presents an important challenge to the machine learning community. Traditional machine learning algorithms may be biased towards the majority class, thus producing poor predictive accuracy over the minority class. In this paper, we describe a new approach that combines boosting, an ensemble-based learning algorithm, with data generation to improve the predictive power of classifiers against imbalanced data sets consisting of two classes. In the DataBoost-IM method, hard examples from both the majority and minority classes are identified during execution of the boosting algorithm. Subsequently, the hard examples are used to separately generate synthetic examples for the majority and minority classes. The synthetic data are then added to the original training set, and the class distribution and the total weights of the different classes in the new training set are rebalanced. The DataBoost-IM method was evaluated, in terms of the F-measures, G-mean and overall accuracy, against seventeen highly and moderately imbalanced data sets using decision trees as base classifiers. Our results are promising and show that the DataBoost-IM method compares well in comparison with a base classifier, a standard benchmarking boosting algorithm and three advanced boosting-based algorithms for imbalanced data set. Results indicate that our approach does not sacrifice one class in favor of the other, but produces high predictions against both minority and majority classes. Class imbalance problems, where the number of samples in each class is unequal, is prevalent in numerous real world machine learning applications. Traditional methods which are biased toward the majority class are ineffective due to the relative severity of misclassifying rare events. This paper proposes a novel evolutionary cluster-based oversampling ensemble framework, which combines a novel cluster-based synthetic data generation method with an evolutionary algorithm (EA) to create an ensemble. The proposed synthetic data generation method is based on contemporary ideas of identifying oversampling regions using clusters. The novel use of EA serves a twofold purpose of optimizing the parameters of the data generation method while generating diverse examples leveraging on the characteristics of EAs, reducing overall computational cost. The proposed method is evaluated on a set of 40 imbalance datasets obtained from the University of California, Irvine, database, and outperforms current state-of-the-art ensemble algorithms tackling class imbalance problems. Classification with imbalanced data-sets has become one of the most challenging problems in Data Mining. Being one class much more represented than the other produces undesirable effects in both the learning and classification processes, mainly regarding the minority class. Such a problem needs accurate tools to be undertaken; lately, ensembles of classifiers have emerged as a possible solution. Among ensemble proposals, the combination of Bagging and Boosting with preprocessing techniques has proved its ability to enhance the classification of the minority class. In this paper, we develop a new ensemble construction algorithm (EUSBoost) based on RUSBoost, one of the simplest and most accurate ensemble, which combines random undersampling with Boosting algorithm. Our methodology aims to improve the existing proposals enhancing the performance of the base classifiers by the usage of the evolutionary undersampling approach. Besides, we promote diversity favoring the usage of different subsets of majority class instances to train each base classifier. Centered on two-class highly imbalanced problems, we will prove, supported by the proper statistical analysis, that EUSBoost is able to outperform the state-of-the-art methods based on ensembles. We will also analyze its advantages using kappa-error diagrams, which we adapt to the imbalanced scenario. Abstract As one of the most challenging and attractive problems in the pattern recognition and machine intelligence field, imbalanced classification has received a large amount of research attention for many years. In binary classification tasks, one class usually tends to be underrepresented when it consists of far fewer patterns than the other class, which results in undesirable classification results, especially for the minority class. Several techniques, including resampling, boosting and cost-sensitive methods have been proposed to alleviate this problem. Recently, some ensemble methods that focus on combining individual techniques to obtain better performance have been observed to present better classification performance on the minority class. In this paper, we propose a novel ensemble framework called Adaptive Ensemble Undersampling-Boost for imbalanced learning. Our proposal combines the Ensemble of Undersampling (EUS) technique, Real Adaboost, cost-sensitive weight modification, and adaptive boundary decision strategy to build a hybrid algorithm. The superiority of our method over other state-of-the-art ensemble methods is demonstrated by experiments on 18 real world data sets with various data distributions and different imbalance ratios. Given the experimental results and further analysis, our proposal is proven to be a promising alternative that can be applied to various imbalanced classification domains.