94 citations · 148 across the 9 of their papers we have counts for
12 papers
How to train your draGAN: A task oriented solution to imbalanced classification
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 O…
Autonomous Cross Domain Adaptation under Extreme Label Scarcity
Weiwei Weng, Mahardhika Pratama, Choiru Za'in +4
A cross domain multistream classification is a challenging problem calling for fast domain adaptations to handle different but related streams in never-ending and rapidly changing…
Unsupervised Continual Learning in Streaming Environments
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. While automatic…
Unsupervised Continual Learning via Self-Adaptive Deep Clustering Approach
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…
Autonomous Deep Quality Monitoring in Streaming Environments
Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer +1
The common practice of quality monitoring in industry relies on manual inspection well-known to be slow, error-prone and operator-dependent. This issue raises strong demand for aut…
Weakly Supervised Deep Learning Approach in Streaming Environments
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. A self-evolving deep neural network, namely Parsimonious Ne…
DEVDAN: Deep Evolving Denoising Autoencoder
Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer +1
The Denoising Autoencoder (DAE) enhances the flexibility of the data stream method in exploiting unlabeled samples. Nonetheless, the feasibility of DAE for data stream analytic des…
Automatic Construction of Multi-layer Perceptron Network from Streaming Examples
Mahardhika Pratama, Choiru Za'in, Andri Ashfahani +2
Autonomous construction of deep neural network (DNNs) is desired for data streams because it potentially offers two advantages: proper model's capacity and quick reaction to drift…
Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments
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…
Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams
Mahardhika Pratama, Andri Ashfahani, Yew Soon Ong +2
The generative learning phase of Autoencoder (AE) and its successor Denosing Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabelled samples. None…
An Online RFID Localization in the Manufacturing Shopfloor
Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer +2
{Radio Frequency Identification technology has gained popularity for cheap and easy deployment. In the realm of manufacturing shopfloor, it can be used to track the location of man…
Big Data Analytic based on Scalable PANFIS for RFID Localization
Choiru Za'in, Mahardhika Pratama, Andri Ashfahani +2
RFID technology has gained popularity to address localization problem in the manufacturing shopfloor due to its affordability and easiness in deployment. This technology is used to…