Comprehensible classification models: a position paper
The vast majority of the literature evaluates the performance of classification models using
only the criterion of predictive accuracy. This paper reviews the case for considering also the …
only the criterion of predictive accuracy. This paper reviews the case for considering also the …
Machine learning applications in river research: Trends, opportunities and challenges
As one of the earth's key ecosystems, rivers have been intensively studied and modelled
through the application of machine learning (ML). With the amount of large data available …
through the application of machine learning (ML). With the amount of large data available …
Explainable artificial intelligence: A survey
In the last decade, with availability of large datasets and more computing power, machine
learning systems have achieved (super) human performance in a wide variety of tasks …
learning systems have achieved (super) human performance in a wide variety of tasks …
A comparison of AutoML tools for machine learning, deep learning and XGBoost
This paper presents a benchmark of supervised Automated Machine Learning (AutoML)
tools. Firstly, we analyze the characteristics of eight recent open-source AutoML tools (Auto …
tools. Firstly, we analyze the characteristics of eight recent open-source AutoML tools (Auto …
Supersparse linear integer models for optimized medical scoring systems
Scoring systems are linear classification models that only require users to add, subtract and
multiply a few small numbers in order to make a prediction. These models are in widespread …
multiply a few small numbers in order to make a prediction. These models are in widespread …
A survey of evolutionary algorithms for decision-tree induction
This paper presents a survey of evolutionary algorithms that are designed for decision-tree
induction. In this context, most of the paper focuses on approaches that evolve decision …
induction. In this context, most of the paper focuses on approaches that evolve decision …
Accurate multi-criteria decision making methodology for recommending machine learning algorithm
Objective Manual evaluation of machine learning algorithms and selection of a suitable
classifier from the list of available candidate classifiers, is highly time consuming and …
classifier from the list of available candidate classifiers, is highly time consuming and …
A survey of multiobjective evolutionary clustering
Data clustering is a popular unsupervised data mining tool that is used for partitioning a
given dataset into homogeneous groups based on some similarity/dissimilarity metric …
given dataset into homogeneous groups based on some similarity/dissimilarity metric …
Multiobjective optimization in bioinformatics and computational biology
This paper reviews the application of multiobjective optimization in the fields of
bioinformatics and computational biology. A survey of existing work, organized by …
bioinformatics and computational biology. A survey of existing work, organized by …
Multi-objective ant colony optimization
Ant colony optimization (ACO) algorithm is one of the most popular swarm-based algorithms
inspired by the behavior of an ant colony to find the shortest path for food. The multi …
inspired by the behavior of an ant colony to find the shortest path for food. The multi …