We propose to solve a label ranking problem as a structured output regression task. In this
view, we adopt a least square surrogate loss approach that solves a supervised learning …

Top-error has become a popular metric for large-scale classification benchmarks due to the
inevitable semantic ambiguity among classes. Existing literature on top-optimization …

In several problems, contrast pattern-based classifiers produce high accuracy and provide
an explanation of the result in terms of the patterns used for classification. However, class …

Exceptional preferences mining (EPM) is a crossover between two subfields of data mining:
local pattern mining and preference learning. EPM can be seen as a local pattern mining …

Decision rules are a powerful tool for the management of information from a relational
database, allowing the extraction of conclusions. A decision algorithm collects a …

In order to fully considered context constraints and eliminate the redundancy of preferences
in the personalized queries in the database, a Top-K conditional preference mining …

This paper proposes a novel label ranker network to learn the relationship between labels to
solve ranking and classification problems. The Preference Neural Network (PNN) uses …

While most existing multilabel ranking methods assume the availability of a single objective
label ranking for each instance in the training set, this paper deals with a more common …

Machine learning applications are challenged by data difficulty factors, which are
responsible for the degradation of data quality and dealing with them is a demanding task …

ABSTRACT Association Rule Mining (ARM) is one of the fundamental components in the
field of data mining that discovers frequent itemsets and interesting relationships for …