Search Results for author: Andri Ashfahani

Found 11 papers, 3 papers with code

How to train your draGAN: A task oriented solution to imbalanced classification

1 code implementation18 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.

imbalanced classification

Autonomous Cross Domain Adaptation under Extreme Label Scarcity

1 code implementation4 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.

Clustering Deep Clustering +1

Unsupervised Continual Learning in Streaming Environments

no code implementations20 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.

Clustering Continual Learning +2

Unsupervised Continual Learning via Self-Adaptive Deep Clustering Approach

no code implementations28 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.

Clustering Continual Learning +1

Autonomous Deep Quality Monitoring in Streaming Environments

no code implementations26 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.

Time Series Analysis

Weakly Supervised Deep Learning Approach in Streaming Environments

no code implementations3 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.

DEVDAN: Deep Evolving Denoising Autoencoder

no code implementations8 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.

Denoising

Automatic Construction of Multi-layer Perceptron Network from Streaming Examples

no code implementations8 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.

General Classification regression

Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments

1 code implementation17 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.

Continual Learning

Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams

no code implementations24 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.

Denoising Incremental Learning

An Online RFID Localization in the Manufacturing Shopfloor

no code implementations20 May 2018 Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer, Qing Cai, Huang Sheng

{Radio Frequency Identification technology has gained popularity for cheap and easy deployment.

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