Current work on Active Learning (AL) tends to assume that the relationship between input and target variables does not change, i.e., the oracle is static. However, oracles can be stream-like and exhibit concept drift, which requires updating the ...

In today’s data-driven world, the timely detection of anomalies in streaming data is of paramount importance across diverse domains, including cybersecurity, industrial processes, and notably, finance. In the financial sector, where transactions ...

Classification learning on non-stationary data may face dynamic changes from time to time. The major problem lies in addressing the imbalance among classes and the substantial cost associated with labeling instances, especially in the presence of ...

The dynamism of our digital universe systems presents a key challenge for predictive analytics. Ensuring the model’s ability to generalize beyond the training data is crucial for reliable predictions. Recent research has primarily focused on ...

In the domain of machine learning and predictive analytics, classifiers hold significant importance as they are instrumental in extracting valuable patterns and enabling precise predictions from data. However, data distributions in streams are ...

Abstract: A comprehensive overview and implementation of a credit card fraud detection system using machine learning for feature selection system. In an era where digital transactions have become the norm, the need for robust fraud detection mechanisms ...

To handle data stream classification with concept drifts, recent studies have shown that a continuously evolving network structure can achieve better performance. However, firstly, they only change one hidden node at a time, which is not enough to ...

A recommendation engine (RE) is a machine learning technique that provides personalized recommendations and anticipates a user's future preference for a collection of goods or services. In Online Supervised Learning (OSL) settings like various REs,...

The concept drift phenomenon describes how the statistical properties of a data distribution change over time. In cybersecurity domain, where data arrives continuously and rapidly in a sequential manner, concept drift can be a significant ...

The complex and changing nature of time series data renders forecasting one of the most challenging tasks in time series analysis. It is also commonly acknowledged that no single ML model can be perfectly appropriate for all applications. One ...

Modern business processes are often characterized by continuous change, which can lead to bias in the results of process mining techniques that assume a static process. This bias is caused by concept drift, which can manifest in many forms and ...

Data stream applications in highly dynamic environments often face concept drift problems, a phenomenon in which the statistical properties of the variables change over time, which can degrade the performance of Machine Learning models. This work ...

Corporate bankruptcy predictions are crucial to companies, investors, and authorities. However, most bankruptcy prediction studies have been based on stationary models, and they tend to ignore important challenges of financial distress like data ...

One of the most crucial components of reducing the risk of serious mental disease is mental health prediction. Meanwhile, the department of public health may apply mental health prediction as a theoretical foundation to develop plans for behavioral ...

Data changing, or drifting, over time is a major problem when using classical machine learning on data streams. One approach to deal with this is to detect changes and react accordingly, for example by retraining the model. Most existing drift ...

Despite the potential of AI in healthcare decision-making, there are also risks to the public for different reasons. Bias is one risk: any data unfairness present in the training set, such as the under-representation of certain minority groups, ...

Bias in machine learning has rightly received significant attention over the past decade. However, most fair machine learning (fair-ML) works to address bias in decision-making systems has focused solely on the offline setting. Despite the wide ...

Concept drift, i.e., the change of the data generating distribution, can render machine learning models inaccurate. Many technologies for learning with drift rely on the interleaved test-train error (ITTE) as a quantity to evaluate model ...

The primary objective of this research endeavor is to tackle the difficult challenge of identifying the elusive aspect peak/trough of the business cycle, which is known to exhibit the tendency of concept drift. To effectively confront this issue, ...

Machine Learning (ML) techniques have been widely applied for network intrusion detection. However, existing ML-based network intrusion detection systems (NIDSs) suffer from fundamental limitations that hinder them from being deployed in the real ...