In recent years, deep learning techniques have outperformed traditional models in many
machine learning tasks. Deep neural networks have successfully been applied to address …

A key aspect of the design of evolutionary and swarm intelligence algorithms is studying
their performance. Statistical comparisons are also a crucial part which allows for reliable …

Species distribution modeling (SDM) is widely used in ecology and conservation. Currently,
the most available data for SDM are species presence‐only records (available through …

Today, intelligent systems that offer artificial intelligence capabilities often rely on machine
learning. Machine learning describes the capacity of systems to learn from problem-specific …

This paper proposes a new metaheuristic optimization algorithm based on ancient war
strategy. The proposed War Strategy Optimization (WSO) is based on the strategic …

Background To evaluate binary classifications and their confusion matrices, scientific
researchers can employ several statistical rates, accordingly to the goal of the experiment …

Rocket achieves state-of-the-art accuracy for time series classification with a fraction of the
computational expense of most existing methods by transforming input time series using …

This paper brings deep learning at the forefront of research into time series classification
(TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of …

Most methods for time series classification that attain state-of-the-art accuracy have high
computational complexity, requiring significant training time even for smaller datasets, and …

Abstract The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE)
is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its …