Melbourne School of Population and Global Health - Research Publications
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ItemCluster-based Diversity Over-sampling: A Density and Diversity Oriented Synthetic Over-sampling for Imbalanced Data(SCITEPRESS - Science and Technology Publications, 2022)In many real-life classification tasks, the issue of imbalanced data is commonly observed. The workings of mainstream machine learning algorithms typically assume the classes amongst underlying datasets are relatively well-balanced. The failure of this assumption can lead to a biased representation of the models’ performance. This has encouraged the incorporation of re-sampling techniques to generate more balanced datasets. However, mainstream re-sampling methods fail to account for the distribution of minority data and the diversity within generated instances. Therefore, in this paper, we propose a data-generation algorithm, Cluster-based Diversity Over-sampling (CDO), to consider minority instance distribution during the process of data generation. Diversity optimisation is utilised to promote diversity within the generated data. We have conducted extensive experiments on synthetic and real-world datasets to evaluate the performance of CDO in comparison with SMOTE-based and diversity-based methods (DADO, DIWO, BL-SMOTE, DB-SMOTE, and MAHAKIL). The experiments show the superiority of CDO.
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ItemReal-time Repatriation: Data Governance for Social Anthropology in the 21st Century(Paris Nanterre University, 2022)Social Anthropologists are currently grappling with complex simultaneous changes in research ethics and data governance regimes across diverse jurisdictions. Internationally, repatriation of unethically acquired ethnographic collections is becoming common-place, resulting in the return of both tangible and intangible cultural assets to their rightful owners. In Europe, the open data movement and the recent implementation of the General Data Protection Regulation appears to challenge social anthropologists’ commitment to protect the confidentiality of oftenvulnerable research participants. Meanwhile, in Australia, New Zealand, Canada and elsewhere, the popular refrain ‘Indigenous data sovereignty’ is compelling many social anthropologists to face discomforting aspects of their field’s involvement in colonial administrative regimes. In this paper I present a model for how social anthropology might reconcile three interrelated factors contributing to this complex situation: 1) Repatriation of data collected from research participants without clear or sufficiently comprehensive consent; 2) Risks and opportunities presented by legislated instances of the open data movement and; 3) Relevant and operable features of the Indigenous data sovereignty movement. Drawing on my 20-year career as a forensic and expert social anthropologist working with Indigenous community organisations on land rights and cultural heritage preservation cases in the Australian Federal Court and under Northern Territory statutory regimes, I illustrate how this model of social anthropological data governance can be put into effect. ‘Real-time repatriation’ describes the synthesis of leading ethical, legal and technological standards in proactively upholding and safeguarding the interests and decision-making autonomy of participants in social anthropological research.
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ItemForensic and Expert Social Anthropology as Cultural Expertise: Developing Professional Standards(Paris-Sorbonne University, 2022)Social anthropology has served as a specific form of cultural expertise in legal-administrative processes since the late 19th century. Since the 1970s, social anthropology has served increasingly as a source of forensic and expert evidence in legal processes in particular, including asylum claims, traditional land ownership and cultural heritage restitution claims, human rights abuse inquiries, and international development initiatives, among others. Notwithstanding this increasing specialisation, internationally recognised professional standards for the provision of forensic and expert social anthropological evidence have remained historically absent. In response, in late 2021, the Royal Anthropological Institute (RAI) commenced development of a professional standards and certification framework for social anthropologists engaged in the provision of forensic and expert evidence to courts and other legally empowered bodies. The RAI initiative represents the first internationally coordinated effort by a professional social anthropological representative organisation to develop such standards. The RAI framework provides a mechanism by which social anthropologists may engage in the provision of forensic and expert services in a relevant, systematic and professionally recognised manner across a range of legal-administrative jurisdictions. The RAI framework establishes terms and definitions for use by both social anthropologists and legal professionals in assessing the prospective relevance of social anthropological services to legal-administrative processes, and in deciding the form that such services should take. This paper will provide a summary of the RAI forensic and expert social anthropology framework and its particular relevance to legal-administrative processes as a specific form of cultural expertise more broadly.
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ItemNo Preview AvailableData Governance and Social Anthropology(European Association of Social Anthropologists, 2021-12-09)
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ItemCollaborative Human-ML Decision Making Using Experts' Privileged Information under Uncertainty( 2021-01-01)Machine Learning (ML) models have been widely applied for clinical decision making. However, in this critical decision making field, human decision making is still prevalent, because clinical experts are more skilled to work with unstructured data specially to deal with uncommon situations. In this paper, we use clinical experts' privileged information as an information source for clinical decision making besides information provided by ML models and introduce a collaborative human-ML decision making model. In the proposed model, two groups of decision makers including ML models and clinical experts collaborate to make a consensus decision. As decision making always comes with uncertainty, we present an interval modelling to capture uncertainty in the proposed collaborative model. For this purpose, clinical experts are asked to give their opinion as intervals, and we generate prediction intervals as the outputs of ML models. Using Interval Agreement Approach (IAA), as an aggregation function in our proposed collaborative model, pave the way to minimize loss of information through aggregating intervals to a fuzzy set. The proposed model not only can improve the accuracy and reliability of decision making, but also can be more interpretable especially when it comes to critical decisions. Experimental results on synthetic data shows the power of the proposed collaborative decision making model in some scenarios.
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ItemNo Preview AvailableFeature Selection using Simulated Annealing with Optimal Neighborhood Approach(IOP Publishing Ltd, 2021-02-15)The one of the metaheuristic approaches that can be used was simulated annealing (SA) algorithm which inspired by annealing metallurgical process. This algorithm shows advantages in finding global optimum of given function which will be used in feature selection. In this study, we will trying to combine the neighborhood size and limited approach by using data simulation comparing between two function which is Akaike Index Criterion (AIC) function and Bayesian Index Criterion (BIC) function. The result of this experiment shows that the selected variables using optimal neighborhood size and limit the selected variable provide the result of goodness model around 98% of accuracy and specificity and 94% of sensitivity compared with simulated annealing algorithms without any modification using both AIC function and BIC function, and in the simulation also shows that BIC function give better result than AIC function.
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ItemNo Preview AvailableThe Spatiotemporal Signature of Indigenous Kinship Networks(Royal Anthropological Institute of Great Britain and Ireland, 2020-09-15)The relationship between social anthropology and geography has been mediated by anthropology's specialised subfield of kinship analysis since the discipline's foundation in the 1870s. As this specialisation evolved with the incorporation of more formal modelling and analysis techniques from the 1960s, and especially with the incorporation of computing from the 1990s, both kinship analysis and geographic information systems have become more precise, with corresponding datasets growing to very large sizes. During the same period in Australia, social anthropology has been adopted by Commonwealth, state, and territory governments as a forensic discipline in the negotiation and litigation of Indigenous land rights via the Federal Court. This turn of events has spurred the development of the new methodology of spatio-temporal kinship network analysis (stKNA), a computer-based technique for modelling Indigenous kinship-based population networks as three-dimensional structures distributed over real geographic space and historical time. Using this methodology, forensic social anthropologists are able to demonstrate the kinship-based cohesion of Indigenous communities over large time-scales, together with their systemic association with the particular geographic regions to which they assert traditional ownership. This paper outlines the methodology of stKNA and its application in Australia.
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ItemNo Preview AvailableIndigenous Australian Population Geometries: Tailoring Geospatial Data Structures for Enhanced Community Service Delivery(Australasian eResearch Organisation, 2020)Data structures used by Australian federal government agencies for spatialising Indigenous geographic population distributions omit critical features necessary for effective service delivery. Currently, the Australian Statistical Geography Standard (ASGS) incorporates a so-called ‘Indigenous Structure’ derived from generic administrative geometries used for statistical purposes in wider population censuses. In this paper we present recent work on the development of an enhanced geometry, derived from linked geographic data and data structures, including Aboriginal community-controlled service providers, land councils, native title claims, language distribution maps, and other pertinent data assets. The objective of developing an enhanced Indigenous population geometry is to provide a maximally useful standard for the coordination and delivery of essential services into Indigenous communities. This work forms part of the core activity of the Indigenous Data Network (IDN) at the Melbourne School of Population and Global Health, University of Melbourne. Staffed by Indigenous and non-Indigenous experts in data science, geoscience, software engineering, population health, epidemiology and social and medical anthropology, the IDN is working to develop and implement best-practice national standards for Indigenous governance of Indigenous data.