Our ability to interrogate and manipulate the genome far exceeds our capacity to measure
the effects of genetic changes on plant traits. Much effort has been made recently by the …

Abstract microRNAs (miRNAs) are small endogenous non-coding RNAs that function as the
universal specificity factors in post-transcriptional gene silencing. Discovering miRNAs …

One of the most important obstacles to deploying predictive models is the fact that humans
do not understand and trust them. Knowing which variables are important in a model's …

Перед вами один из самых интересных учебников по машинному обучению–разделу
искусственного интеллекта, изучающего методы построения моделей, способных …

As one of the most comprehensive machine learning texts around, this book does justice to
the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's …

Subgroup discovery is a data mining technique which extracts interesting rules with respect
to a target variable. An important characteristic of this task is the combination of predictive …

This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and
subgroup discovery (SD) in a unifying framework named supervised descriptive rule …

Subgroup discovery is a broadly applicable descriptive data mining technique for identifying
interesting subgroups according to some property of interest. This article summarizes …

This paper focuses on the problem of defect prediction, a problem of major importance
during software maintenance and evolution. It is essential for software developers to identify …

This paper provides an analysis of the behavior of separate-and-conquer or covering rule
learning algorithms by visualizing their evaluation metrics and their dynamics in coverage …