arXiv subjects

Soheil Abadifard

Publications and source records attributed to Soheil Abadifard.

3 recordsLinked to original sources

arXiv2026

From Field Data to Global Food Systems Intelligence: A Semantic Graph Framework for Sustainable Wheat Production

In response to the growing need for structured, interoperable agricultural data, this paper presents the Sustainable Wheat Production Datahub, a modular, graph-based framework that brings diverse wheat production datasets together into a single, queryable store. Using the Knowledge Acquisition and Representation Methodology (KNARM), with domain experts in the loop, we developed ontologies for nutrient management and disease management, two of the areas that most affect wheat yield and its sustainability, covering practices such as nitrogen fertilization and fungicide-based disease control. The two ontologies are federated, meaning they are maintained as separate but connected modules, joined by a bridging layer of cross-domain links, and together they form the schema of a knowledge graph (KG). We construct this KG by populating the ontologies with data from diverse sources, including field trials, expert knowledge, and environmental descriptors. We validate the ontologies, showing the KG accurately answers practical agronomic questions expressed in SPARQL. We demonstrate that the KG derives facts entailed by the ontologies beyond those explicitly stored, and that a single query can draw across independently sourced datasets. The framework also has practical implications for wheat research and extension programs, since it makes data from different sources easier to find, combine, and reuse. Designed to expand toward the full wheat lifecycle, from farm to table, this work establishes the foundation for a scalable, semantically rich global food systems datahub.

cs.AI↗

arXiv2025

LSH-DynED: A Dynamic Ensemble Framework with LSH-Based Undersampling for Evolving Multi-Class Imbalanced Classification

The classification of imbalanced data streams, which have unequal class distributions, is a key difficulty in machine learning, especially when dealing with multiple classes. While binary imbalanced data stream classification tasks have received considerable attention, only a few studies have focused on multi-class imbalanced data streams. Effectively managing the dynamic imbalance ratio is a key challenge in this domain. This study introduces a novel, robust, and resilient approach to address these challenges by integrating Locality Sensitive Hashing with Random Hyperplane Projections (LSH-RHP) into the Dynamic Ensemble Diversification (DynED) framework. To the best of our knowledge, we present the first application of LSH-RHP for undersampling in the context of imbalanced non-stationary data streams. The proposed method undersamples the majority classes by utilizing LSH-RHP, provides a balanced training set, and improves the ensemble's prediction performance. We conduct comprehensive experiments on 23 real-world and ten semi-synthetic datasets and compare LSH-DynED with 15 state-of-the-art methods. The results reveal that LSH-DynED outperforms other approaches in terms of both Kappa and mG-Mean effectiveness measures, demonstrating its capability in dealing with multi-class imbalanced non-stationary data streams. Notably, LSH-DynED performs well in large-scale, high-dimensional datasets with considerable class imbalances and demonstrates adaptation and robustness in real-world circumstances. To motivate our design, we review existing methods for imbalanced data streams, outline key challenges, and offer guidance for future work. For the reproducibility of our results, we have made our implementation available on GitHub.

cs.LG↗

arXiv2023

DynED: Dynamic Ensemble Diversification in Data Stream Classification

Ensemble methods are commonly used in classification due to their remarkable performance. Achieving high accuracy in a data stream environment is a challenging task considering disruptive changes in the data distribution, also known as concept drift. A greater diversity of ensemble components is known to enhance prediction accuracy in such settings. Despite the diversity of components within an ensemble, not all contribute as expected to its overall performance. This necessitates a method for selecting components that exhibit high performance and diversity. We present a novel ensemble construction and maintenance approach based on MMR (Maximal Marginal Relevance) that dynamically combines the diversity and prediction accuracy of components during the process of structuring an ensemble. The experimental results on both four real and 11 synthetic datasets demonstrate that the proposed approach (DynED) provides a higher average mean accuracy compared to the five state-of-the-art baselines.

cs.LG↗