1 code implementation • 18 Nov 2022 • Leon O. Guertler, Andri Ashfahani, Anh Tuan Luu
The long-standing challenge of building effective classification models for small and imbalanced datasets has seen little improvement since the creation of the Synthetic Minority Over-sampling Technique (SMOTE) over 20 years ago.
1 code implementation • 4 Sep 2022 • Weiwei Weng, Mahardhika Pratama, Choiru Za'in, Marcus de Carvalho, Rakaraddi Appan, Andri Ashfahani, Edward Yapp Kien Yee
This paper aims to attack the problem of extreme label shortage in the cross domain multistream classification problems where only very few labelled samples of the source stream are provided before process runs.
no code implementations • 20 Sep 2021 • Andri Ashfahani, Mahardhika Pratama
A deep clustering network is desired for data streams because of its aptitude in extracting natural features thus bypassing the laborious feature engineering step.
no code implementations • 28 Jun 2021 • Mahardhika Pratama, Andri Ashfahani, Edwin Lughofer
Unsupervised continual learning remains a relatively uncharted territory in the existing literature because the vast majority of existing works call for unlimited access of ground truth incurring expensive labelling cost.
no code implementations • 26 Jun 2021 • Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer, Edward Yapp Kien Yee
The common practice of quality monitoring in industry relies on manual inspection well-known to be slow, error-prone and operator-dependent.
no code implementations • 3 Nov 2019 • Mahardhika Pratama, Andri Ashfahani, Mohamad Abdul Hady
The feasibility of existing data stream algorithms is often hindered by the weakly supervised condition of data streams.
no code implementations • 8 Oct 2019 • Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer, Yew Soon Ong
The Denoising Autoencoder (DAE) enhances the flexibility of the data stream method in exploiting unlabeled samples.
no code implementations • 8 Oct 2019 • Mahardhika Pratama, Choiru Za'in, Andri Ashfahani, Yew Soon Ong, Weiping Ding
The advantage of NADINE, namely elastic structure and online learning trait, is numerically validated using nine data stream classification and regression problems where it demonstrates performance improvement over prominent algorithms in all problems.
1 code implementation • 17 Oct 2018 • Andri Ashfahani, Mahardhika Pratama
The feasibility of deep neural networks (DNNs) to address data stream problems still requires intensive study because of the static and offline nature of conventional deep learning approaches.
no code implementations • 24 Sep 2018 • Mahardhika Pratama, Andri Ashfahani, Yew Soon Ong, Savitha Ramasamy, Edwin Lughofer
The generative learning phase of Autoencoder (AE) and its successor Denosing Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabelled samples.
no code implementations • 20 May 2018 • Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer, Qing Cai, Huang Sheng
{Radio Frequency Identification technology has gained popularity for cheap and easy deployment.