The L2 convergence of stream data mining algorithms based on probabilistic neural networks
Authors:
- Danuta Rutkowska,
- Piotr Duda,
- Jinde Cao,
- Leszek Rutkowski,
- Aleksander Byrski,
- Maciej Jaworski,
- Dacheng Tao
Abstract
This paper concerns a new incremental approach to mining data streams. It is known that patterns in a data stream may evolve over time. In many cases, we need to track and analyze the nature of these changes. In the paper, the probabilistic neural networks are considered as basic models for tracking changes in data streams. We present globally convergent stream data mining algorithms applied to problems of regression, classification, and density estimation in a time-varying (drifting) environment. The algorithms are derived from the Parzen kernel-based probabilistic neural networks working in the online mode. For each problem, a theorem is presented ensuring the convergence of the algorithm designed for tracking drifting regression, density, or discriminant functions. Illustrative examples explain in detail how to choose the bandwidth of the Parzen kernel and the learning rate of the online algorithm. The performance of all algorithms is shown in exemplary simulations. It should be noted that this paper is one of very few, in the existing literature, presenting mathematically justified stream data mining algorithms.
- Record ID
- CUT91752494be084d1aa71817c15a83f6db
- Publication categories
- ;
- Author
- Journal series
- Information Sciences, ISSN 0020-0255, e-ISSN 1872-6291
- Issue year
- 2023
- Vol
- 631
- Pages
- 346-368
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 367-368; Bibliografia (liczba pozycji) - 34; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 631
- Keywords in English
- stream data mining, global convergence, Parzen kernel
- ASJC Classification
- ; ; ; ; ;
- DOI
- DOI:10.1016/j.ins.2023.02.074 Opening in a new tab
- URL
- https://www.sciencedirect.com/science/article/abs/pii/S0020025523002645 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 200
- Score source
- journalList
- Score
- Publication indicators
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT91752494be084d1aa71817c15a83f6db/
- URN
urn:pkr-prod:CUT91752494be084d1aa71817c15a83f6db
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.