In this paper we present a general overview of several supervised machine learning (ML)
algorithms and illustrate their use for the prediction of mass mortality events in the coastal …

A tree-ensemble method, referred to as time series forest (TSF), is proposed for time series
classification. TSF employs a combination of entropy gain and a distance measure, referred …

Research into the classification of time series has made enormous progress in the last
decade. The UCR time series archive has played a significant role in challenging and …

Time series classification is an important task with many challenging applications. A nearest
neighbor (NN) classifier with dynamic time warping (DTW) distance is a strong solution in …

Classification of time series has been attracting great interest over the past decade. While
dozens of techniques have been introduced, recent empirical evidence has strongly …

Symbolic learning is the logic-based approach to machine learning, and its mission is to
provide algorithms and methodologies to extract logical information from data and express it …

Temporal data mining deals with the harvesting of useful information from temporal data.
New initiatives in health care and business organizations have increased the importance of …

Access control is often reported to be" profoundly broken" in real-world practices due to
prevalent policy misconfigurations introduced by system administrators (sysadmins). Given …

Compared with expert systems for specific disease diagnosis, knowledge-based systems to
assist decision making in triage usually try to cover a much wider domain but can use a …

This paper proposes an extension of classification trees to time series input variables. A new
split criterion based on time series proximities is introduced. First, the criterion relies on an …