arXiv:2603.28394v1 Announce Type: cross
Abstract: Network performance modeling is a field that predates early computer networks and the beginning of the Internet. It aims to predict the traffic performance of packet flows in a given network. Its appli…
arXiv:2603.28420v1 Announce Type: new
Abstract: Neural networks are fundamental tools of modern machine learning. The standard paradigm assumes binary interactions (across feedforward linear passes) between inter-tangled units, organized in sequential…
arXiv:2603.28357v1 Announce Type: cross
Abstract: The accurate classification of brain tumors from MRI scans is essential for effective diagnosis and treatment planning. This paper presents a weighted ensemble learning approach that combines deep lear…
arXiv:2603.28417v1 Announce Type: new
Abstract: This paper proposes a new univariate filter feature selection (FFS) algorithm called KGroups. The majority of work in the literature focuses on investigating the relevance or redundancy estimations of fe…
arXiv:2603.28336v1 Announce Type: cross
Abstract: Systematic literature reviews in the social sciences overwhelmingly follow arborescent logics — hierarchical keyword filtering, linear screening, and taxonomic classification — that suppress the late…
arXiv:2603.28416v1 Announce Type: new
Abstract: Reinforcement learning algorithms are defined by their learning update rules, which are typically hand-designed and fixed. We present an evolutionary framework for discovering reinforcement learning algo…
arXiv:2603.28315v1 Announce Type: cross
Abstract: Thyroid nodule classification using ultrasound imaging is essential for early diagnosis and clinical decision-making; however, despite promising performance on in-distribution data, existing deep learn…
arXiv:2603.28410v1 Announce Type: new
Abstract: Preference-based many-objective optimization faces two obstacles: an expanding space of trade-offs and heterogeneous, context-dependent human value structures. Towards this, we propose a Bayesian framewo…
arXiv:2603.28257v1 Announce Type: cross
Abstract: KAN-PCA is an autoencoder that uses a KAN as encoder and a linear map as decoder. It generalizes classical PCA by replacing linear projections with learned B-spline functions on each edge. The motivati…
arXiv:2603.28396v1 Announce Type: new
Abstract: Machine Learning (ML)-based detectors are becoming essential to counter the proliferation of malware. However, common ML algorithms are not designed to cope with the dynamic nature of real-world settings…