arXiv subjects

George Georgoulas

Publications and source records attributed to George Georgoulas.

3 recordsLinked to original sources

arXiv2026

K-IPO: Kendall-constrained Importance Preserving Oversampling for Imbalanced Tabular Data

Oversampling is widely used to address class imbalance in tabular classification, but existing methods can distort the feature importance ranking underlying model explanations. Although recent studies have quantified this distortion by comparing real and synthetic data, none have actively sought to prevent it. In this paper, we introduce Kendall-constrained Importance-Preserving Oversampling (K-IPO), a generator-agnostic, "generate-then-select" framework that preserves the original data's feature importance ranking during augmentation. K-IPO iteratively generates minority-class candidates and accepts them only if their inclusion maintains a user-defined minimum Kendall's tau (τ) correlation with the reference ranking. Optionally, stricter constraints can be applied to the highest-ranked features. We evaluated K-IPO on 20 imbalanced binary classification datasets using three classifiers and multiple explanation methods. In most cases, K-IPO achieved the best or tied-best results in feature importance preservation, explanation consistency, and class separability. It also generally improved predictive performance while maintaining competitive computational overhead.

cs.LG↗

arXiv2026

A Survey on Data-Driven Models for Soil Moisture Regression and Classification

Soil Moisture (SM) modelling constitutes a complex spatiotemporal learning problem characterised by nonlinear environmental interactions, heterogeneous data sources, and limited ground observations. Physics-based approaches, such as water balance models, rely on explicit hydrological equations and high-quality inputs, but their computational cost and scalability limitations restrict large-scale deployment. Data-driven artificial intelligence (AI) methods have emerged as flexible alternatives, enabling the extraction of empirical relationships between soil moisture and environmental variables with reduced modelling assumptions. This work presents a structured survey of AI-based models for soil moisture estimation and classification. Existing approaches are organized into five categories: (a) statistical time-series models, (b) geostatistical methods (c) classical machine learning (ML) models, (d) Deep Learning (DL) models and (e) Probabilistic/Bayesian methods. These models leverage historical soil moisture records, meteorological variables, vegetation indices, topography, soil characteristics, and geolocation data to perform regression or classification tasks.

cs.LG↗

arXiv2019

Visual Area Coverage with Attitude-Dependent Camera Footprints by Particle Harvesting

In aerial visual area coverage missions, the camera footprint changes over time based on the camera position and orientation -- a fact that complicates the whole process of coverage and path planning. This article proposes a solution to the problem of visual coverage by filling the target area with a set of randomly distributed particles and harvesting them by camera footprints. This way, high coverage is obtained at a low computational cost. In this approach, the path planner considers six degrees of freedom (DoF) for the camera movement and commands thrust and attitude references to a lower layer controller, while maximizing the covered area and coverage quality. The proposed method requires a priori information of the boundaries of the target area and can handle areas of very complex and highly non-convex geometry. The effectiveness of the approach is demonstrated in multiple simulations in terms of computational efficiency and coverage.

cs.RO↗