Chang Wei Tan

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

Doctor of Philosophy

About

43
Publications
20,348
Reads
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501
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 (43)
Preprint
Full-text available
Detecting critical moments, such as emotional outbursts or changes in decisions during conversations, is crucial for understanding shifts in human behavior and their consequences. Our work introduces a novel problem setting focusing on these moments as turning points (TPs), accompanied by a meticulously curated, high-consensus, human-annotated mult...
Article
Full-text available
We argue that time series analysis is fundamentally different in nature to either vision or natural language processing with respect to the forms of meaningful self-supervised learning tasks that can be defined. Motivated by this insight, we introduce a novel approach called Series2Vec for self-supervised representation learning. Unlike the state-o...
Article
Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series analysis where the relevant features must often be abstracted from the raw data but are not known a pri...
Article
Purpose Despite availability of commercial EEG software for automated epileptiform detection, validation on real-world EEG datasets is lacking. Performance evaluation of two software packages on a large EEG dataset of patients with genetic generalized epilepsy was performed. Methods Three epileptologists labelled IEDs manually of EEGs from three c...
Article
Full-text available
Transformers have demonstrated outstanding performance in many applications of deep learning. When applied to time series data, transformers require effective position encoding to capture the ordering of the time series data. The efficacy of position encoding in time series analysis is not well-studied and remains controversial, e.g., whether it is...
Preprint
Full-text available
Transformers have demonstrated outstanding performance in many applications of deep learning. When applied to time series data, transformers require effective position encoding to capture the ordering of the time series data. The efficacy of position encoding in time series analysis is not well-studied and remains controversial, e.g., whether it is...
Preprint
Full-text available
The measurement of progress using benchmarks evaluations is ubiquitous in computer science and machine learning. However, common approaches to analyzing and presenting the results of benchmark comparisons of multiple algorithms over multiple datasets, such as the critical difference diagram introduced by Dem\v{s}ar (2006), have important shortcomin...
Article
Full-text available
Dynamic time warping ( DTW ) is a popular time series distance measure that aligns the points in two series with one another. These alignments support warping of the time dimension to allow for processes that unfold at differing rates. The distance is the minimum sum of costs of the resulting alignments over any allowable warping of the time dimens...
Preprint
Full-text available
Time series classification (TSC) is a challenging task due to the diversity of types of feature that may be relevant for different classification tasks, including trends, variance, frequency, magnitude, and various patterns. To address this challenge, several alternative classes of approach have been developed, including similarity-based, features...
Preprint
Full-text available
Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series analysis where the relevant features must often be abstracted from the raw data but are not known a pri...
Preprint
Full-text available
Dynamic Time Warping (DTW) is a popular time series distance measure that aligns the points in two series with one another. These alignments support warping of the time dimension to allow for processes that unfold at differing rates. The distance is the minimum sum of costs of the resulting alignments over any allowable warping of the time dimensio...
Article
Full-text available
Nearest neighbour similarity measures are widely used in many time series data analysis applications. They compute a measure of similarity between two time series. Most applications require tuning of these measures’ meta-parameters in order to achieve good performance. However, most measures have at least $$O(L^2)$$ O ( L 2 ) complexity, making the...
Article
Objective: Automated interictal epileptiform discharge (IED) detection has been widely studied, with machine learning methods at the forefront in recent years. As computational resources become more accessible, researchers have applied deep learning (DL) to IED detection with promising results. This systematic review aims to provide an overview of...
Article
Full-text available
Deep learning for automated interictal epileptiform discharge (IED) detection has been topical with many published papers in recent years. All existing works viewed EEG signals as time-series and developed specific models for IED classification; however, general time-series classification (TSC) methods were not considered. Moreover, none of these m...
Preprint
Full-text available
Feature extraction methods help in dimensionality reduction and capture relevant information. In time series forecasting (TSF), features can be used as auxiliary information to achieve better accuracy. Traditionally, features used in TSF are handcrafted, which requires domain knowledge and significant data-engineering work. In this research, we fir...
Article
Full-text available
The application of deep learning approaches for the detection of inter-ictal epileptiform discharges is a nascent field, with most studies published in the past 5 years. Although many recent models have been published demonstrating promising results, deficiencies in descriptions of datasets, unstandardized methods, variation in performance evaluati...
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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