A Robust Dynamic Classifier Selection Approach for Hyperspectral Images with Imprecise Label Information

Supervised hyperspectral image (HSI) classification relies on accurate label information. However, it is not always possible to collect perfectly accurate labels for training samples. This motivates the development of classifiers that are sufficiently robust to some reasonable amounts of errors in data labels. Despite the growing importance of this aspect, it has not been sufficiently studied in the literature yet. In this paper, we analyze the effect of erroneous sample labels on probability distributions of the principal components of HSIs, and provide in this way a statistical analysis of the resulting uncertainty in classifiers. Building on the theory of imprecise probabilities, we develop a novel robust dynamic classifier selection (R-DCS) model for data classification with erroneous labels. Particularly, spectral and spatial features are extracted from HSIs to construct two individual classifiers for the dynamic selection, respectively. The proposed R-DCS model is based on the robustness of the classifiers’ predictions: the extent to which a classifier can be altered without changing its prediction. We provide three possible selection strategies for the proposed model with different computational complexities and apply them on three benchmark data sets. Experimental results demonstrate that the proposed model outperforms the individual classifiers it selects from and is more robust to errors in labels compared to widely adopted approaches.

Multi-Classifier Approaches for Supporting Clinical Decision Making

Diagnosis is one of the most important processes in the medical field. Since the knowledge domains of clinical specialties are expanding rapidly in terms of complexity and volume of data, clinicians have, in many cases, difficulties to make an accurate diagnosis. Therefore, intelligent and quantitative support for diagnostic tasks can be effectively exploited for improving the effectiveness of the process and reduce misdiagnosis. In this respect, Multi-Classifier Systems represent one of the most promising approaches within Machine Learning methodologies. This paper proposes a Multi-Classifier Systems framework for supporting diagnostic activities with the aim of improving diagnostic accuracy. The framework uses and combines several classification algorithms by dynamically selecting the most competent classifier according to the test sample and its location in the feature space. Here, we extend our previous research. The new experimental results, compared with several multi classifier techniques, based on dynamic classifier selection, on classification datasets, show that the performance of the proposed framework exceeds the state-of-the-art dynamic classifier selection techniques.