Complexity and performance analysis of supervised machine learning models for applied technologies : an experimental study with impulsive [alpha]-stable noise

Ahmed, Areeb (author), Bosnić, Zoran (author)

Impulsive alpha (α)-stable noise, characterized by heavy tails and intense outliers, is a key ingredient in simulating financial, medical, seismic, and digital communication technologies. It poses versatile challenges to conventional machine learning (ML) algorithms in predicting noise parameters for multidisciplinary artificial intelligence (AI)-embedded devices. In this study, we adopted a two-phase methodology to investigate the complexity and performance of supervised ML algorithms while classifying impulsive noise parameters. We generated synthetic datasets of α-stable noise distributions for experimentation in a controlled environment. It was followed by experimental evaluation to derive the complexity and performance of ML classifiers—k-nearest neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), and Random Forest (RF). Moreover, we employed a very high channel noise level of −15 dB in the test datasets to ensure that the derived analysis applies to real-world devices. The results demonstrate the high performance of DT and RF in structured binary classification of the α regime and the sign of skewness, while incurring satisfactory computational costs. However, SVM and kNN are comparatively more robust for multi-class classification, albeit with higher memory and training costs. On the contrary, NB fails to address the skewed and impulsive behavior of α-stable noise. We observed that even the most effective classifiers struggle to achieve perfect accuracy in multi-class classification. Overall, the experimental results reveal significant trade-off relationships between the complexity and performance of ML classifiers. Conclusively, simple models are well-suited for coarse-grained tasks, such as α-approximation and sign-of-skewness classification. In contrast, sophisticated models can be deployed to predict noise parameters to some extent. Our study provides a clear set of trade-offs for future applied AI devices that address adversarial and impulsive noise.

• Language: English
• Keywords: supervised machine learning, complexity, performance, SVM, kNN, NB, DT, random forest, α stable distributions, impulsive noise
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: 24 str.
• Numbering: Vol. 14, iss. 5, art. 252
• PID: 20.500.12556/RUL-182226
• UDC: 004.85
• ISSN on article: 2227-7080
• DOI: 10.3390/technologies14050252
• COBISS.SI-ID: 276850179
• Publication date in RUL: 04.05.2026

Record is a part of a journal

• Title: Technologies
• Shortened title: Technologies
• Publisher: MDPI AG
• ISSN: 2227-7080
• COBISS.SI-ID: 523413017

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: nadzorovano strojno učenje, kompleksnost, zmogljivost, podporni vektorski stroji, k-najbližjih sosedov, naivni Bayesov klasifikator, odločitveno drevo, naključni gozd, α-stabilne porazdelitve

Projects

• Funder: EC - European Commission
• Project number: 101081355
• Name: Machine learning for Sciences and Humanities
• Acronym: SMASH