Dynamic Ensemble Selection with Regional Expertise

Many recent works have shown that ensemble methods yield better generalizability over single classifier approach by aggregating the decisions of all base learners in machine learning tasks. To address the redundancy and inaccuracy issues with the base learners in ensemble methods, classifier/ensemble selection methods have been proposed to select one single classifier or an ensemble (a subset of all base learners) to classify a query pattern. This final classifier or ensemble is determined either statically before prediction or dynamically for every query pattern during prediction. Static selection approaches select classifier and ensemble by evaluating classifiers in terms of accuracy and diversity. While dynamic classifier/ensemble selection (DCS, DES) methods incorporate local information for a dedicated classifier/ensemble to each query pattern. Our work focuses on DES by proposing a new DES framework — DES with Regional Expertise (DES-RE). The success of a DES system lies in two factors: the quality of base learners and the optimality of ensemble selection. DES-RE proposed in our work addresses these two challenges respectively. 1) Local expertise enhancement. A novel data sampling and weighting strategy that combines the advantages of bagging and boosting is employed to increase the local expertise of the base learners in order to facilitate the later ensemble selection. 2) Competence region optimization. DES-RE tries to learn a distance metric to form better competence regions (aka neighborhood) that promote strong base learners with respect to a specific query pattern. In addition to perform local expertise enhancement and competence region optimization independently, we proposed an expectation–maximization (EM) framework that combines the two procedures. For all the proposed algorithms, extensive simulations are conducted to validate their performances

Diversified Ensemble Classifiers for Highly Imbalanced Data Learning and their Application in Bioinformatics

In this dissertation, the problem of learning from highly imbalanced data is studied. Imbalance data learning is of great importance and challenge in many real applications. Dealing with a minority class normally needs new concepts, observations and solutions in order to fully understand the underlying complicated models. We try to systematically review and solve this special learning task in this dissertation.We propose a new ensemble learning framework—Diversified Ensemble Classifiers for Imbal-anced Data Learning (DECIDL), based on the advantages of existing ensemble imbalanced learning strategies. Our framework combines three learning techniques: a) ensemble learning, b) artificial example generation, and c) diversity construction by reversely data re-labeling. As a meta-learner, DECIDL utilizes general supervised learning algorithms as base learners to build an ensemble committee. We create a standard benchmark data pool, which contains 30 highly skewed sets with diverse characteristics from different domains, in order to facilitate future research on imbalance data learning. We use this benchmark pool to evaluate and compare our DECIDL framework with several ensemble learning methods, namely under-bagging, over-bagging, SMOTE-bagging, and AdaBoost. Extensive experiments suggest that our DECIDL framework is comparable with other methods. The data sets, experiments and results provide a valuable knowledge base for future research on imbalance learning. We develop a simple but effective artificial example generation method for data balancing. Two new methods DBEG-ensemble and DECIDL-DBEG are then designed to improve the power of imbalance learning. Experiments show that these two methods are comparable to the state-of-the-art methods, e.g., GSVM-RU and SMOTE-bagging. Furthermore, we investigate learning on imbalanced data from a new angle—active learning. By combining active learning with the DECIDL framework, we show that the newly designed Active-DECIDL method is very effective for imbalance learning, suggesting the DECIDL framework is very robust and flexible.Lastly, we apply the proposed learning methods to a real-world bioinformatics problem—protein methylation prediction. Extensive computational results show that the DECIDL method does perform very well for the imbalanced data mining task. Importantly, the experimental results have confirmed our new contributions on this particular data learning problem