Chang Wei Tan

Chang Wei Tan
Monash University (Australia) · Clayton School of Information Technology

Doctor of Philosophy

About

23
Publications
9,391
Reads
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97
Citations
Introduction
I am a Research Fellow in Machine Learning at Monash University. My research interests involve scalable data mining, time series analysis and applying machine learning to real-life engineering problems.
Additional affiliations
July 2015 - June 2018
Monash University (Australia)
Position
  • PhD Student
July 2015 - present
Monash University (Australia)
Position
  • Teaching Associate
Description
  • Lab demonstrator for first year engineering unit, ENG1002
June 2014 - December 2015
Monash University (Australia)
Position
  • Research Assistant
Education
July 2015 - June 2018
Monash University (Australia)
Field of study
  • Data Mining and Machine Learning
February 2011 - April 2015
Monash University (Australia)
Field of study
  • Electrical and Computer Systems Engineering

Publications

Publications (23)
Article
Full-text available
We propose MultiRocket, a fast time series classification (TSC) algorithm that achieves state-of-the-art accuracy with a tiny fraction of the time and without the complex ensembling structure of many state-of-the-art methods. MultiRocket improves on MiniRocket, one of the fastest TSC algorithms to date, by adding multiple pooling operators and tran...
Conference Paper
Full-text available
Epilepsy diagnostic investigation involving manual visual analysis of electroencephalogram (EEG) is a time-consuming process. Deep neural networks, especially the convolutional network (CNN), have been applied to interictal epileptiform discharge (IED) detection and have achieved promising results. However, these networks do not incorporate clinica...
Preprint
Full-text available
Epilepsy diagnostic investigation involving manual visual analysis of electroencephalogram (EEG) is a time-consuming process. Deep neural networks, especially the convolutional network (CNN), have been applied to interictal epileptiform discharge (IED) detection and have achieved promising results. However, these networks do not incorporate clinica...
Article
Full-text available
This paper studies time series extrinsic regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable; a task closely related to time series classification (TSC), which aims to learn the relationship between a time series and a categorical class label. This task generalize...
Preprint
Epilepsy is the most common neurological disorder. The diagnosis commonly requires manual visual electroencephalogram (EEG) analysis which is time-consuming. Deep learning has shown promising performance in detecting interictal epileptiform discharges (IED) and may improve the quality of epilepsy monitoring. However, most of the datasets in the lit...
Conference Paper
Full-text available
Epilepsy is the most common neurological disorder. The diagnosis commonly requires manual visual electroencephalogram (EEG) analysis which is time-consuming. Deep learning has shown promising performance in detecting interictal epileptiform discharges (IED) and may improve the quality of epilepsy monitoring. However, most of the datasets in the lit...
Chapter
Full-text available
Epilepsy is the most common neurological disorder. The diagnosis commonly requires manual visual electroencephalogram (EEG) analysis which is time-consuming. Deep learning has shown promising performance in detecting interictal epileptiform discharges (IED) and may improve the quality of epilepsy monitoring. However, most of the datasets in the lit...
Preprint
Full-text available
Rocket and MiniRocket, while two of the fastest methods for time series classification, are both somewhat less accurate than the current most accurate methods (namely, HIVE-COTE and its variants). We show that it is possible to significantly improve the accuracy of MiniRocket (and Rocket), with some additional computational expense, by expanding th...
Preprint
Full-text available
This paper introduces Time Series Regression (TSR): a little-studied task of which the aim is to learn the relationship between a time series and a continuous target variable. In contrast to time series classification (TSC), which predicts a categorical class label, TSR predicts a numerical value. This task generalizes forecasting, relaxing the req...
Preprint
Full-text available
Time series research has gathered lots of interests in the last decade, especially for Time Series Classification (TSC) and Time Series Forecasting (TSF). Research in TSC has greatly benefited from the University of California Riverside and University of East Anglia (UCR/UEA) Time Series Archives. On the other hand, the advancement in Time Series F...
Preprint
Full-text available
In this study we demonstrate a novel Brain Computer Interface (BCI) approach to detect driver distraction events to improve road safety. We use a commercial wireless headset that generates EEG signals from the brain. We collected real EEG signals from participants who undertook a 40-minute driving simulation and were required to perform different t...
Article
Full-text available
In recent years, many new ensemble-based time series classification (TSC) algorithms have been proposed. Each of them is significantly more accurate than their predecessors. The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is currently the most accurate TSC algorithm when assessed on the UCR repository. It is a meta-en...
Preprint
Full-text available
Research into time series classification has tended to focus on the case of series of uniform length. However, it is common for real-world time series data to have unequal lengths. Differing time series lengths may arise from a number of fundamentally different mechanisms. In this work, we identify and evaluate two classes of such mechanisms -- var...
Preprint
Full-text available
There has been renewed recent interest in developing effective lower bounds for Dynamic Time Warping (DTW) distance between time series. These have many applications in time series indexing, clustering, forecasting, regression and classification. One of the key time series classification algorithms, the nearest neighbor algorithm with DTW distance...
Conference Paper
Full-text available
Time series classification maps time series to labels. The nearest neighbour algorithm (NN) using the Dynamic Time Warping (DTW) similarity measure is a leading algorithm for this task and a component of the current best ensemble classifiers for time series. However, NN-DTW is only a winning combination when its meta-parameter – its warping window...
Conference Paper
Full-text available
Tamping is a maintenance procedure which repacks ballast particles under sleepers in order to restore the correct geometrical position of ballasted tracks. Tamping is often used when geometrical issues are first identified. However, historical data often shows that tamping is not always the most appropriate, or ef-fective, form of maintenance to re...
Conference Paper
Railway maintenance planning is critical in maintaining track assets. Tamping is a common railway maintenance procedure and is often used when geometrical issues are first identified. Tamping repacks ballast particles under sleepers to restore the correct geometrical position of ballasted tracks. However, historical data shows that tamping is not a...

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