META-DES: A Dynamic Ensemble Selection Framework using Meta-Learning (1810.01270v1)

Abstract: Dynamic ensemble selection systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. This is achieved by defining a criterion to measure the level of competence of a base classifier, such as, its accuracy in local regions of the feature space around the query instance. However, using only one criterion about the behavior of a base classifier is not sufficient to accurately estimate its level of competence. In this paper, we present a novel dynamic ensemble selection framework using meta-learning. We propose five distinct sets of meta-features, each one corresponding to a different criterion to measure the level of competence of a classifier for the classification of input samples. The meta-features are extracted from the training data and used to train a meta-classifier to predict whether or not a base classifier is competent enough to classify an input instance. During the generalization phase, the meta-features are extracted from the query instance and passed down as input to the meta-classifier. The meta-classifier estimates, whether a base classifier is competent enough to be added to the ensemble. Experiments are conducted over several small sample size classification problems, i.e., problems with a high degree of uncertainty due to the lack of training data. Experimental results show the proposed meta-learning framework greatly improves classification accuracy when compared against current state-of-the-art dynamic ensemble selection techniques.

• The paper presents a meta-learning solution for dynamic ensemble selection that evaluates classifier competence using diverse meta-features.
• It reframes ensemble selection as a meta-problem by employing five distinct meta-feature sets to capture local accuracy, consensus, and confidence.
• Experimental results on 30 datasets show that META-DES outperforms static ensembles and traditional DES methods, especially in data-scarce conditions.

A Meta-Learning-Based Framework for Dynamic Ensemble Selection

The paper "META-DES: A Dynamic Ensemble Selection Framework using Meta-Learning" presents an innovative framework for improving the performance of ensemble classifiers through dynamic ensemble selection (DES), addressing the challenge of classifier competence assessment. The authors propose a meta-learning approach as a solution to enhance classification accuracy, particularly in scenarios with limited data, by dynamically selecting an ensemble of classifiers tailored to each test instance.

Dynamic Ensemble Selection and Meta-Learning

Dynamic ensemble selection aims to select the most competent classifiers from a pool for each test instance by measuring their competence in the local region of the feature space. However, traditional DES techniques may rely heavily on a single criterion, such as local accuracy, which can be insufficient for achieving optimal performance. The proposed META-DES framework diverges by utilizing multiple criteria, encapsulated as meta-features, to estimate classifier competence more robustly.

In META-DES, the DES problem is re-framed as a meta-problem. The meta-problem is defined by meta-features, which capture various properties related to classifier behavior. The meta-features derive from five proposed sets, each representing distinct competence criteria, such as local accuracy, consensus among classifiers, and confidence levels. This approach applies meta-learning to predict whether a classifier is competent for a given test instance, thereby equipping the DES system with a comprehensive understanding of classifier reliability.

Experimentation and Results

The META-DES framework was evaluated extensively across 30 datasets, chosen from repositories such as UCI, STATLOG, and KEEL. The experimental results demonstrate that META-DES frequently outperforms both static ensemble methods and other state-of-the-art DES techniques. Notably, META-DES achieved superior accuracy in many datasets, especially those characterized by a limited number of training samples. This advantage can be attributed to the enhanced data availability for training the meta-classifier, as the meta-learning framework generates numerous meta-feature vectors from each training sample.

Challenges such as classifier selection in environments where the consensus among classifiers is low were overcome by specifically focusing the meta-classifier training data on these instances, optimizing the system's robustness in uncertain conditions.

Implications and Future Directions

The implications of this work are significant for areas requiring high classification reliability in uncertain and data-scarce environments. The introduction of meta-learning into DES represents a methodological advancement that could inspire further research into diverse meta-features and more sophisticated meta-classifiers, thereby refining DES systems' adaptability and accuracy.

Future research directions proposed by the authors include the exploration of new meta-feature sets and optimization techniques to enhance the effectiveness of competence estimation. Additionally, investigating alternative training paradigms for the meta-classifier could yield further improvements in the recognition performance of dynamic selection systems.

In conclusion, the META-DES framework offers a more robust, accurate method for dynamic ensemble selection by integrating the strengths of meta-learning, significantly advancing the field of ensemble classifier systems.