Online learning with feature spaces that are not fixed but can vary over time renders a
seemingly flexible learning paradigm thus has drawn much attention. Unfortunately, two …

Short text information has considerable commercial value and immeasurable social value.
Natural language processing and short text sentiment analysis technology can organize and …

This paper explores a new online learning problem where the input sequence lives in an
over-time varying feature space and the ground-truth label of any input point is given only …

In many large-scale machine learning applications, data are accumulated over time, and
thus, an appropriate model should be able to update in an online style. In particular, it would …

Recently, learning and mining from data streams with incremental feature spaces have
attracted extensive attention, where data may dynamically expand over time in both volume …

Continual learning from streaming data sources becomes more and more popular due to the
increasing number of online tools and systems. Dealing with dynamic and everlasting …

In dynamic business environments, the underlying true data pattern changes rapidly.
Machine learning models built upon historical data may not be responsive to the changes. A …

Addressing the pervasive challenge of missing data in data science and research, this paper
introduces a novel approach for imputing missing values. Our method heuristically …

In streaming data applications, the underlying concept often changes with time which
necessitates the update of employed classifiers. Most approaches in the literature utilize the …

In dynamic business environments, the underlying true data pattern changes rapidly.
Machine learning models built upon historical data may not be responsive to the changes. A …