On Meta-Learning for Dynamic Ensemble Selection
This addresses the problem of improving classification accuracy in ensemble systems for machine learning practitioners, though it appears incremental as it builds on existing dynamic ensemble selection methods.
The paper tackles dynamic ensemble selection by proposing a meta-learning framework with five meta-feature sets to predict classifier competence, finding that a problem-dependent training scenario yields the best results and outperforms state-of-the-art techniques.
In this paper, we propose a novel dynamic ensemble selection framework using meta-learning. The framework is divided into three steps. In the first step, the pool of classifiers is generated from the training data. The second phase is responsible to extract the meta-features and train the meta-classifier. Five distinct sets of meta-features are proposed, each one corresponding to a different criterion to measure the level of competence of a classifier for the classification of a given query sample. The meta-features are computed using the training data and used to train a meta-classifier that is able to predict whether or not a base classifier from the pool is competent enough to classify an input instance. Three different training scenarios for the training of the meta-classifier are considered: problem-dependent, problem-independent and hybrid. Experimental results show that the problem-dependent scenario provides the best result. In addition, the performance of the problem-dependent scenario is strongly correlated with the recognition rate of the system. A comparison with state-of-the-art techniques shows that the proposed-dependent approach outperforms current dynamic ensemble selection techniques.