Developing effective and efficient data stream classifiers is challenging for the machine
learning community because of the dynamic nature of data streams. As a result, many data …

Novelty detection in data streams is the task of detecting concepts that were not known prior,
in streams of data. Many machine learning algorithms have been proposed to detect these …

New class detection and effective model expansion are of great importance in incremental
data mining. In open incremental data environments, data often come with novel classes, eg …

Continual learning from data streams is among the most important topics in contemporary
machine learning. One of the biggest challenges in this domain lies in creating algorithms …

Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects
from general samples with numerous high-stake applications including fraud detection and …

This paper presents the simultaneous utilization of video images and inertial signals that are
captured at the same time via a video camera and a wearable inertial sensor within a fusion …

Class-Incremental Learning (CIL) aims to train a reliable model with the streaming data,
which emerges unknown classes sequentially. Different from traditional closed set learning …

In the modern era of digitization, the analysis in the Internet of Things (IoT) environment
demands a brisk amalgamation of domains such as high-dimension (images) data sensing …

In this paper, we propose MUSE-RNN, a multilayer self-evolving recurrent neural network
model for real-time classification of streaming data. Unlike the existing approaches, MUSE …

We study the problem of active intrusion detection over network traffic streams. Existing
works create clusters for known classes and manually label instances outside the clusters …