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A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
TLDR
We propose a taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based. Expand
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An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
TLDR
Training classifiers with datasets which suffer of imbalanced class distributions is an important problem in data mining. Expand
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Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
TLDR
We present a case study which involves a set of techniques in classification tasks and we study the use of nonparametric statistical inference for analyzing the results obtained in an experiment design. Expand
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An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes
TLDR
We analyse the performance of ensemble methods by binarization techniques for multi-class classification problems. Expand
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EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
TLDR
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. Expand
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A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability
TLDR
The experimental analysis on the performance of a proposed method is a crucial and necessary task to carry out in a research. Expand
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SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
TLDR
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. Expand
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A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets
TLDR
We study the behaviour of fuzzy rule based classification systems in the framework of imbalanced data-sets, focusing on the synergy with the preprocessing mechanisms of instances and the configuration of fuzzy Rule Based Classification systems. Expand
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On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on Intrusion Detection Systems
TLDR
Linguistic labels enable smoother borderline, and allows higher interpretability.Divide-and-conquer learning scheme, improves precision for rare attack events. Expand
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Learning from Imbalanced Data Sets
TLDR
Data Science is a discipline for discovering new and significant relationships, patterns and trends in the examination of large amounts of data. Expand
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