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Found 6 papers, 3 papers with code
Mitigating shortage of labeled data using clustering-based active learning with diversity exploration
In this paper, we proposed a new clustering-based active learning framework, namely Active Learning using a Clustering-based Sampling (ALCS), to address the shortage of labeled data.
DA$^{\textbf{2}}$-Net : Diverse & Adaptive Attention Convolutional Neural Network
Standard Convolutional Neural Network (CNN) designs rarely focus on the importance of explicitly capturing diverse features to enhance the network's performance.
A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering
In this paper, a supervised feature selection method using density-based feature clustering (SFSDFC) is proposed to obtain an appropriate final feature subset for mixed-type data.
A Clustering-based Framework for Classifying Data Streams
The overlap among classes and the labeling of data streams constitute other major challenges for classifying data streams.
Evolving Multi-label Classification Rules by Exploiting High-order Label Correlation
The correlation between labels can be exploited at different levels such as capturing the pair-wise correlation or exploiting the higher-order correlations.
Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural Network
Results show that the proposed model prediction accuracy remains satisfactory and outperforms the other approaches despite the extent of anomalies and noise-induced in the data.
