Learning Semi-Naive Bayesian Classifiers from Numeric Data. This project addresses research priority 3, offering frontier technologies. It will deliver better and faster classification technologies that greatly help accomplish many real-world tasks including medical diagnosis, fraud detection, spam filtering and webpage search, where accurate and fast classification is critical to save life, increase efficiency, reduce crime and conserve resources. Hence this project addresses priority 4 as well ....Learning Semi-Naive Bayesian Classifiers from Numeric Data. This project addresses research priority 3, offering frontier technologies. It will deliver better and faster classification technologies that greatly help accomplish many real-world tasks including medical diagnosis, fraud detection, spam filtering and webpage search, where accurate and fast classification is critical to save life, increase efficiency, reduce crime and conserve resources. Hence this project addresses priority 4 as well, better safeguarding Australia from disease and crime. This project will also support a young research group of international standing. It will train the involved researchers to attain a high level of proficiency and excellence in machine learning research and development.Read moreRead less
Investigation and development of robust rule discovery and classification system. This research focuses on a national research priority, namely smart information use. The expected outcomes of the project will greatly advance intelligent system design, such as automatic decision making, fault detection and problem diagnosis, for finance, medical, telecom and many other areas. It has great potential for commercialisation and earning incomes for the future research. The publications will benefit th ....Investigation and development of robust rule discovery and classification system. This research focuses on a national research priority, namely smart information use. The expected outcomes of the project will greatly advance intelligent system design, such as automatic decision making, fault detection and problem diagnosis, for finance, medical, telecom and many other areas. It has great potential for commercialisation and earning incomes for the future research. The publications will benefit the future development of intelligent systems for dealing with missing data. This project directly supports a PhD student and two research assistants who will most likely continue their higher degree study. These contribute to regional tertiary education.Read moreRead less
Personalised Ontology Learning and Mining for Web Information Gathering. The project will provide a flexible framework for a sound theoretical model of personalised systems. It will significantly influence the development of personalised Web services and many leading industry organisations that attempt to deliver personalised services to their valuable customers. The proposed project will also strengthen the pre-existing international collaboration networks. It will establish Australian researc ....Personalised Ontology Learning and Mining for Web Information Gathering. The project will provide a flexible framework for a sound theoretical model of personalised systems. It will significantly influence the development of personalised Web services and many leading industry organisations that attempt to deliver personalised services to their valuable customers. The proposed project will also strengthen the pre-existing international collaboration networks. It will establish Australian researchers leading position in the related research fields and communities, and provide an established paradigm for other researchers to follow. In addition, the project will provide significant contributions to Australian National Research Priority in the areas of Smart Information Use.Read moreRead less
Discovering justified knowledge from data. Knowledge discovery from data has assumed a critical role in numerous areas of science, commerce and public administration. However, its effectiveness is limited by the undesirable propensity of current techniques to make many false, as well as real, discoveries. This research will rectify that problem, a critical outcome given the potential cost of making decisions or setting policy using flawed information. For example, it may prevent the adoption of ....Discovering justified knowledge from data. Knowledge discovery from data has assumed a critical role in numerous areas of science, commerce and public administration. However, its effectiveness is limited by the undesirable propensity of current techniques to make many false, as well as real, discoveries. This research will rectify that problem, a critical outcome given the potential cost of making decisions or setting policy using flawed information. For example, it may prevent the adoption of ineffective strategies for addressing land degradation; inappropriately targeted public health expenditure; expensive development and clinical trialing of drugs which prove ineffective; and wasted police and security investigations into unfounded suspicions of criminal or terrorist activity.Read moreRead less
Fuzzy Transfer Learning for Prediction in Data-Shortage and Rapidly-Changing Environments. Collecting sufficient up-to-date data to train a learning model for data analysis and prediction is difficult and expensive. This project will develop a Fuzzy Transfer Learning methodology, using Information Granularity theory, that exploits data with different features and/or distributions available in other, similar systems, to provide accurate learning-based prediction for current problems. It will esta ....Fuzzy Transfer Learning for Prediction in Data-Shortage and Rapidly-Changing Environments. Collecting sufficient up-to-date data to train a learning model for data analysis and prediction is difficult and expensive. This project will develop a Fuzzy Transfer Learning methodology, using Information Granularity theory, that exploits data with different features and/or distributions available in other, similar systems, to provide accurate learning-based prediction for current problems. It will establish a new research direction, Fuzzy Transfer Learning for Prediction, and the outcomes will enable government and industry to better use past experience to make more accurate predictions and decisions. Highly significant benefits will also accrue in the data analytics, business intelligence and decision making research fields.Read moreRead less
Improving human reasoning with causal Bayes networks: a multimodal approach. This project aims to improve human causal and probabilistic reasoning about complex systems by taking a user-centric, multimodal, interactive approach. The project will explore new integrated visual and verbal ways of explaining a causal probabilistic model and its reasoning, to reduce known human reasoning difficulties, and investigate how to reduce cognitive load by prioritising the most useful user- and context-speci ....Improving human reasoning with causal Bayes networks: a multimodal approach. This project aims to improve human causal and probabilistic reasoning about complex systems by taking a user-centric, multimodal, interactive approach. The project will explore new integrated visual and verbal ways of explaining a causal probabilistic model and its reasoning, to reduce known human reasoning difficulties, and investigate how to reduce cognitive load by prioritising the most useful user- and context-specific information. Expected outcomes include novel AI methods that empower users to drive the reasoning process and strengthen trust in the system’s reasoning. Performance will be assessed in medical and legal domains, with significant potential benefits to end users from better, more transparent reasoning and decision making.Read moreRead less
Multi-dimensional Temporal Abstraction to Support Neonatal Clinical Research. Each year, the death of a baby causes grief for thousands of Australian parents, contributes to depression and considerable anxiety in the population. In this work we propose procedures that will significantly reduce this unhappy scenario. The availability of a complex trend and pattern analysis will give Neonatologists access to predictive clinical analysis that has not previously been available locally or internation ....Multi-dimensional Temporal Abstraction to Support Neonatal Clinical Research. Each year, the death of a baby causes grief for thousands of Australian parents, contributes to depression and considerable anxiety in the population. In this work we propose procedures that will significantly reduce this unhappy scenario. The availability of a complex trend and pattern analysis will give Neonatologists access to predictive clinical analysis that has not previously been available locally or internationally. Thus, significant benefits in terms of lower mortality rates and lower long-term disability rates among babies requiring special care is possible. This research will provide the basis for future projects that will support regional hospitals.Read moreRead less
Formalising and automating the elicitation and reconciliation of requirements from multiple stakeholders. It is well recognised that requirements specifications are often error-prone and that it is much cheaper to detect and fix these errors early in the software development life cycle than later. A major problem with requirements determination is that each and every stakeholder has his/her own representation of the enterprise reality. This project seeks to take these views and use set-theore ....Formalising and automating the elicitation and reconciliation of requirements from multiple stakeholders. It is well recognised that requirements specifications are often error-prone and that it is much cheaper to detect and fix these errors early in the software development life cycle than later. A major problem with requirements determination is that each and every stakeholder has his/her own representation of the enterprise reality. This project seeks to take these views and use set-theoretical techniques from Formal Concept Analysis (FCA) to automatically generate and compare the underlying conceptual models. A process model based on FCA has been proposed which we will extend and empirically evaluate in this project. The result will be a more rigorous and yet pragmatic approach to requirements engineering which offers the greatest economic leverage.Read moreRead less
Incremental Knowledge Acquisition for Machine Translation from Multiple Experts. With increasing globalisation and an increasing amount of electronically available documents the need for machine translation is growing dramatically. The state-of-the-art in machine translation is still far from satisfactory. Substantial post-editing is necessary for most non-technical texts and even for many technical documents to make the translation really understandable. This project will develop a new approach ....Incremental Knowledge Acquisition for Machine Translation from Multiple Experts. With increasing globalisation and an increasing amount of electronically available documents the need for machine translation is growing dramatically. The state-of-the-art in machine translation is still far from satisfactory. Substantial post-editing is necessary for most non-technical texts and even for many technical documents to make the translation really understandable. This project will develop a new approach for buildingmachine translation systems by extending the unorthodox approach of Ripple-Down Rules, which proved very successful for building expert systems in the medical domain.It is intended to build a machine translation system by integrating the knowledge from many experts.Read moreRead less
Explaining the outcomes of complex computational models. This project aims to develop new algorithms that automatically generate explanations for the results produced by complex computational models. In recent times, these models have become increasingly accurate, and hence pervasive. However, the reasoning of Deep Neural Networks and Bayesian Networks, and of complex Regression models and Decision Trees is often unclear, impairing effective decision making by practitioners who use the results o ....Explaining the outcomes of complex computational models. This project aims to develop new algorithms that automatically generate explanations for the results produced by complex computational models. In recent times, these models have become increasingly accurate, and hence pervasive. However, the reasoning of Deep Neural Networks and Bayesian Networks, and of complex Regression models and Decision Trees is often unclear, impairing effective decision making by practitioners who use the results of these models or investigate the decisions made by the systems. Practical benefits of clear decision making reasoning by complex computational models include reduced risk, increased productivity and revenue, appropriate adoption of technologies including improved education for practitioners, and improved outcomes for end users. Significant benefits will be demonstrated through the evaluations with practitioners in the areas of healthcare and energy.Read moreRead less