Feature selection technique is a knowledge discovery tool which provides an understanding
of the problem through the analysis of the most relevant features. Feature selection aims at …

Microarray data classification is a difficult challenge for machine learning researchers due to
its high number of features and the small sample sizes. Feature selection has been soon …

Mass spectrometry-based proteomic technique has become indispensable in current
exploration of complex and dynamic biological processes. Instrument development has …

Predictive models ground many state-of-the-art developments in statistical brain image
analysis: decoding, MVPA, searchlight, or extraction of biomarkers. The principled approach …

Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …

Defect prediction models help software quality assurance teams to allocate their limited
resources to the most defect-prone modules. Model validation techniques, such as-fold …

Defect prediction models-classifiers that identify defect-prone software modules-have
configurable parameters that control their characteristics (eg, the number of trees in a …

Understanding the relationships between the physicochemical properties of engineered
nanomaterials and their toxicity is critical for environmental and health risk analysis …

Knowledge of tree species composition in a forest is an important topic in forest
management. Accurate tree species maps allow for much more detailed and in-depth …

Background Comparative assessment of treatment results in paediatric hepatoblastoma
trials has been hampered by small patient numbers and the use of multiple disparate …