From the 1 of 6 linked papers with an AI index.
6 papers
Synthesizing real-world distributions from high-dimensional Gaussian Noise with Fully Connected Neural Network
Joanna Komorniczak
The paper introduces a fully connected neural network that converts high‑dimensional Gaussian noise into synthetic tabular data resembling real datasets, using preprocessing, PCA,…
How well does Classification Accuracy capture Concept Drift Detection Quality? An overview of Concept Drift Detection evaluation
Joanna Komorniczak
Data streams are nowadays among the most frequently analyzed data structures, with the concept drift posing a major challenge encountered by processing systems. Despite the proposi…
Open World Autoencoding Drift Detection with Novel Class Recognition in Tabular Non-stationary Data Streams
Joanna Komorniczak
Data stream processing has become a landmark in modern machine learning applications, with concept drifts and novel class appearances posing the primary challenges faced by sophist…
Transforming Datasets to Requested Complexity with Projection-based Many-Objective Genetic Algorithm
Joanna Komorniczak
The research community continues to seek increasingly more advanced synthetic data generators to reliably evaluate the strengths and limitations of machine learning methods. This w…
Synthetic Non-stationary Data Streams for Recognition of the Unknown
Joanna Komorniczak
The problem of data non-stationarity is commonly addressed in data stream processing. In a dynamic environment, methods should continuously be ready to analyze time-varying data --…
Describing Nonstationary Data Streams in Frequency Domain
Joanna Komorniczak
Concept drift is among the primary challenges faced by the data stream processing methods. The drift detection strategies, designed to counteract the negative consequences of such…