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Machine learning for data streams with CapyMOA
(Springer, 2026) Sun, Yibin; Gomes, Heitor Murilo; Lee, Anton; Gunasekara, Nuwan; Cassales, Guilherme; Liu, Justin Jia; Heyden, Marco; Cerqueira, Vitor; Bahri, Maroua; Koh, Yun Sing; Pfahringer, Bernhard; Bifet, Albert
The exponential growth of data in recent decades has underscored the need for high-speed, real-time, and adaptive processing in machine learning. Data stream learning provides an effective framework to address this challenge. This article introduces CapyMOA, an open-source library designed specifically for data stream learning, offering powerful tools for building and deploying adaptive ML models. GitHub: https://github.com/adaptive-machine-learning/CapyMOA. Website: https://capymoa.org.
Streaming Isolation Forest
(Springer, 2025) Liu, Justin Jia; Cassales, Guilherme; Liu, Fei T.; Pfahringer, Bernhard; Bifet, Albert
Anomaly detection is crucial to identify unusual patterns in various domains. In particular, continuous and rapid flow creates distinct challenges within streaming data. This paper introduces the Streaming Isolation Forest (SiForest), a novel algorithm that uses isolation principles and reservoir sampling to align the model with current data distributions. SiForest efficiently detects anomalies with minimal computational and memory requirements and dynamically updates its model using a subtree regrowing strategy. Empirical evaluation on twenty-three benchmark datasets demonstrates that SiForest outperforms eight state-of-the-art algorithms in terms of AUC-ROC scores, achieving greater precision and adaptability.
Tools for self- or peer-assessment of interprofessional competencies of healthcare students: A scoping review
(Frontiers, 2024) Brownie, Sharon; Yap, Jia Rong; Blanchard, Denise; Amankwaa, Issac; Pearce, Amy; Sampath, Kesava Kovanur; Yan, Ann-Rong; Andersen, Patrea; Broman, Patrick
Introduction: Healthcare professionals are expected to demonstrate competence in the effective management of chronic disease and long-term health and rehabilitation needs. Care provided by groups of collaborating professionals is currently well recognized as a more effective way to support people living with these conditions than routine, single-profession clinical encounters. Clinical learning contexts provide hands-on opportunities to develop the interprofessional competencies essential for health professional students in training; however, suitable assessment tools are needed to support student attainment of interprofessional competencies with self-assessment espoused as an important component of learning. Method: A structured approach was taken to locate and review existing tools used for the self-assessment and peer assessment of students’ competencies relevant to interprofessional practice. Results: A range of self- and/or peer assessment approaches are available, including formally structured tools and less structured processes inclusive of focus groups and reflection. Discussion: The identified tools will usefully inform discussion regarding interprofessional competency self- and peer assessment options by healthcare students participating in a broad range of clinical learning contexts. Conclusion: Self- and/or peer assessment is a useful approach for those seeking to effectively enhance interprofessional learning and measure the attainment of related competencies.
Adaptive Isolation Forest
(Springer, 2025) Liu, Justin Jia; Cassales, Guilherme; Liu, Fei T.; Pfahringer, Bernhard; Bifet, Albert
Anomaly detection in real-world data streams often struggles with concept drift, where evolving data distributions challenge algorithms to maintain a balance between accuracy, speed, stability, and plasticity. We present Adaptive Isolation Forest (AIF), a novel anomaly detection algorithm designed to effectively adapt to such changes in a resource-efficient and balanced manner. AIF’s novelty can be found in the combination of a smart model update mechanism together with a newly developed MinTreeMaxMass (MTMM) criterion, which scores individual trees for replacement. Extensive evaluations on various benchmark datasets demonstrate that AIF significantly outperforms existing state-of-the-art streaming anomaly detection algorithms in terms of detection accuracy. Moreover, AIF achieves linear time and space complexities, providing a robust solution that maintains high accuracy and efficiency, balancing stability and plasticity in dynamic data streams.
On the Fringes: Identity development for taitamāhine Māori in one secondary kura auraki
(The University of Waikato, 2026-09-29) Johnston, Emma; Berryman, Mere; Virtue, Arihia
Despite Aotearoa NZ’s progress in the last 50 years towards enacting Te Tiriti o Waitangi and realising educational success for Māori as Māori, remnants of colonial ideologies remain in educational structures, systems, pedagogies and curriculums. The intergenerational cultural degradation that Māori have experienced through 200 years of racialised and assimilative educational policies are being exacerbated through the current government’s educational reforms. This research shows that educators and schools have the power to and must pushback against the government’s systemic attacks on the mana of Te Tiriti o Waitangi and Māori identity. The strength of this thesis lies in the voices of the participants; the students, their mothers and teachers, which are presented through collaborative stories. Together they illustrate the barriers to positive tribal identity development for taitamāhine Māori as being cultural essentialism, racial micro and macroaggressions, and teachers not taking full agency to address these. The enablers to positive Māori tribal identity are identified as unfettered support and access to te ao and mātauranga Māori, positive peer recognition as Māori, culturally safe and responsive learning environments, and the conscientisation and agentic discursive (re)positioning of teachers to become agents of change. This thesis uses a metaphor of weaving to illustrate how a kākahu or cloak of self-sustaining positive tribal identity for taitamāhine Māori can be nurtured and woven through secondary education. When school leaders and educators power-share with Māori and listen to the voices of whānau, hapū, iwi, and work with Māori institutions and organisations within the framework of Ka Hikitia – Ka Hāpaitia, they can support the tribal identity of taitamāhine Māori to flourish. While this research focusses on experiences in one urban single-sex girl’s secondary kura auraki, the findings have implications for all schools and educators committed to addressing inequity in education.