Coupling different methods for overcoming the class imbalance problem

Many classification problems must deal with imbalanced datasets where one class \u2013 the majority class \u2013 outnumbers the other classes. Standard classification methods do not provide accurate predictions in this setting since classification is generally biased towards the majority class. The minority classes are oftentimes the ones of interest (e.g., when they are associated with pathological conditions in patients), so methods for handling imbalanced datasets are critical. Using several different datasets, this paper evaluates the performance of state-of-the-art classification methods for handling the imbalance problem in both binary and multi-class datasets. Different strategies are considered, including the one-class and dimension reduction approaches, as well as their fusions. Moreover, some ensembles of classifiers are tested, in addition to stand-alone classifiers, to assess the effectiveness of ensembles in the presence of imbalance. Finally, a novel ensemble of ensembles is designed specifically to tackle the problem of class imbalance: the proposed ensemble does not need to be tuned separately for each dataset and outperforms all the other tested approaches. To validate our classifiers we resort to the KEEL-dataset repository, whose data partitions (training/test) are publicly available and have already been used in the open literature: as a consequence, it is possible to report a fair comparison among different approaches in the literature. Our best approach (MATLAB code and datasets not easily accessible elsewhere) will be available at https://www.dei.unipd.it/node/2357

Local Rule-Based Explanations of Black Box Decision Systems

The recent years have witnessed the rise of accurate but obscure decision systems which hide the logic of their internal decision processes to the users. The lack of explanations for the decisions of black box systems is a key ethical issue, and a limitation to the adoption of machine learning components in socially sensitive and safety-critical contexts. %Therefore, we need explanations that reveals the reasons why a predictor takes a certain decision. In this paper we focus on the problem of black box outcome explanation, i.e., explaining the reasons of the decision taken on a specific instance. We propose LORE, an agnostic method able to provide interpretable and faithful explanations. LORE first leans a local interpretable predictor on a synthetic neighborhood generated by a genetic algorithm. Then it derives from the logic of the local interpretable predictor a meaningful explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance's features that lead to a different outcome. Wide experiments show that LORE outperforms existing methods and baselines both in the quality of explanations and in the accuracy in mimicking the black box

Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification

We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include the support vector machine, Gaussian process classifiers, random subspace of adaboost, random subspace of rotation boosting, and deep learning classifiers. We demonstrate that a heterogeneous ensemble based on the simple fusion by sum rule of different classifiers performs consistently well across all twenty-five datasets. The most important result of our investigation is demonstrating that some very recent approaches, including the heterogeneous ensemble we propose in this paper, are capable of outperforming an SVM classifier (implemented with LibSVM), even when both kernel selection and SVM parameters are carefully tuned for each dataset

TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System

Intrusion detection systems (IDS) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles is proposed. An hybrid feature selection technique comprising three methods, i.e. particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensembles based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. Results regarding the UNSW-NB15 dataset also improve the ones achieved by several state of the art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier