A global optimisation approach to classification in medical diagnosis and prognosis
- Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
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- Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Description: 2003003950
Argumentation structures that integrate dialectical and non-dialectical reasoning
- Authors: Stranieri, Andrew , Zeleznikow, John , Yearwood, John
- Date: 2001
- Type: Text , Journal article
- Relation: Knowledge Engineering Review Vol. 16, no. 4 (Dec 2001), p. 331-348
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- Description: Argumentation concepts have been applied to numerous knowledge engineering endeavours in recent years. For example, a variety of logics have been developed to represent argumentation in the context of a dialectical situation such as a dialogue. In contrast to the dialectical approach, argumentation has also been used to structure knowledge. This can be seen as a non-dialectical approach. The Toulmin argument structure has often been used to structure knowledge non-dialectically yet most studies that apply the Toulmin structure do not use the original structure but vary one or more components. Variations to the Toulmin structure can be understood as different ways to integrate a dialectical perspective with a non-dialectical one. Drawing the dialectical/non-dialectical distinction enables the specification of a framework called the generic actual argument model that is expressly non-dialectical. The framework enables the development of knowledge-based systems that integrate a variety of inference procedures, combine information retrieval with reasoning and facilitate automated document drafting. Furthermore, the non-dialectical framework provides the foundation for simple dialectical models. Systems based on our approach have been developed in family law, refugee law, determining eligibility for government legal aid, copyright law and e-tourism.
- Description: C1
- Description: 2003002516
Discovering interesting association rules from legal databases
- Authors: Ivkovic, Sasha , Yearwood, John , Stranieri, Andrew
- Date: 2002
- Type: Text , Journal article
- Relation: Information & Communication Technology Law Vol. 11, no. 1 (2002), p. 35-47
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- Description: The Knowledge Discovery from Databases (KDD) technique called 'association rules' is applied to a large data set representing applicants for government-funded legal aid. Results indicate that KDD can be an invaluable tool for legal analysts. Association rules discovered identify associations between variables that are present in the data set though are not necessarily causal. Interesting rules can prompt analysts to formulate hypotheses for further investigation. The identification of interesting rules is typically performed using an objective measure of 'interesting' although this measure is often not sufficiently accurate to eliminate all uninteresting rules. In this article, a subjective measure of interestingness is adopted in conjunction with the objective measures. This leads to the ability to focus more accurately on those rules that surprise the analyst and are therefore more likely to be interesting. In general, KDD techniques have not been applied to law despite possible benefits because data is often stored in narrative form rather than in structured databases. However, the impending introduction of data warehouses that collect data from a number of organizations across a legal system presents invaluable opportunities for analysts using KDD.
- Description: C1
- Description: 2003000037
Generic arguments : A framework for supporting online deliberative discourse
- Authors: Yearwood, John , Stranieri, Andrew
- Date: 2002
- Type: Text , Conference paper
- Relation: Paper presented at the Thirteenth Australasian Conference on Information Systems, Melbourne : 4th December, 2002
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- Description: In this paper we propose a framework based on argumentation that can be used to support deliberative discourse on line. Online communities have several distinct advantages as very open forums but they also have some deep disadvantages. We argue that the proposed framework and web application GAAMtalk permits and encourages the positive elements of online deliberation that will enhance discussions.
- Description: E1
- Description: 2003000114
Are schools of education failing the tertiary mathematics sector?
- Authors: Mays, Heather , Yearwood, John
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at 2003 International Symposium on Information Technology, Las Vegas, USA : 28th April, 2003
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- Description: In the 1990s, two major issues emerged globally for tertiary mathematics educators: declining standards in the mathematical proficiency of students at tertiary-entry level and the advocates for the incorporation of IT into the processes of teaching and learning. Addressing these issues required significant reform of both curriculum content and classroom practice to ensure that the technology was used appropriately and effectively. These reforms were largely implemented by staff from tertiary Schools of Mathematics. Rather than leading the reform, the Education community lagged behind and in some instances has taken research into the use of IT in Mathematics Education in a questionable direction. In this paper, we outline what we contend has been a failure on the behalf of the Education community to tackle and address problems experienced by mathematics educators (particularly at the tertiary level) and make some suggestions for the directions of future research in Mathematics Education.
- Description: E1
- Description: 2003000464
DOWL : A dynamic ontology language
- Authors: Avery, John , Yearwood, John
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at IADIS International Conference WWW/Internet 2003, Algarve, Portugal : 5th August, 2003
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- Description: Abstract: Ontologies in a web setting, particularly those used in a group context (such as a virtual community), need to be flexible and open to changes that reflect the evolution of knowledge. OWL the ontology language of the semantic web provides very little for facilitating the description of evolutionary changes in an ontology. We propose a dynamic web ontology language (dOWL), an extension to OWL, which consists of a set of elements that can be used to model these evolutionary changes in an ontology.
- Description: E1
- Description: 2003000552
New algorithms for multi-class cancer diagnosis using tumor gene expression signatures
- Authors: Bagirov, Adil , Ferguson, Brent , Ivkovic, Sasha , Saunders, Gary , Yearwood, John
- Date: 2003
- Type: Text , Journal article
- Relation: Bioinformatics Vol. 19, no. 14 (2003), p. 1800-1807
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- Description: Motivation: The increasing use of DNA microarray-based tumor gene expression profiles for cancer diagnosis requires mathematical methods with high accuracy for solving clustering, feature selection and classification problems of gene expression data. Results: New algorithms are developed for solving clustering, feature selection and classification problems of gene expression data. The clustering algorithm is based on optimization techniques and allows the calculation of clusters step-by-step. This approach allows us to find as many clusters as a data set contains with respect to some tolerance. Feature selection is crucial for a gene expression database. Our feature selection algorithm is based on calculating overlaps of different genes. The database used, contains over 16 000 genes and this number is considerably reduced by feature selection. We propose a classification algorithm where each tissue sample is considered as the center of a cluster which is a ball. The results of numerical experiments confirm that the classification algorithm in combination with the feature selection algorithm perform slightly better than the published results for multi-class classifiers based on support vector machines for this data set.
- Description: C1
- Description: 2003000439
Unsupervised and supervised data classification via nonsmooth and global optimisation
- Authors: Bagirov, Adil , Rubinov, Alex , Sukhorukova, Nadezda , Yearwood, John
- Date: 2003
- Type: Text , Journal article
- Relation: Top Vol. 11, no. 1 (2003), p. 1-92
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- Description: We examine various methods for data clustering and data classification that are based on the minimization of the so-called cluster function and its modications. These functions are nonsmooth and nonconvex. We use Discrete Gradient methods for their local minimization. We consider also a combination of this method with the cutting angle method for global minimization. We present and discuss results of numerical experiments.
- Description: C1
- Description: 2003000421
A modular framework for multi category feature selection in digital mammography
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at ESANN 2004 Proceedings: European Symposium on Artificial Neural Networks, Bruges, Belguim : 28/04/2004 Vol. Elsevier, p. 175-180
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- Description: Many existing researches utilized many different approaches for recognition in digital mammography using various ANN classifier-modeling techniques. Different types of feature extraction techniques are also used. It has been observed that, beyond a certain point, the inclusion of additional features leads to a worse rather than better performance. Moreover, the choice of features to represent the patterns affects several aspects of pattern recognition problem such as accuracy, required learning time and necessary number of samples. A common problem with the multi category feature classification is the conflict between the categories. None of the feasible solutions allow simultaneous optimal solution for all categories. In order to find an optimal solutions the searching space can be divided based on individual category in each sub region and finally merging them through decision spport system. In this paper we propose a canonical GA based modular feature selection approach combined with standard MLP.
- Description: E1
- Description: 2003000872
An experiment in task decomposition and ensembling for a modular artificial neural network
- Authors: Ferguson, Brent , Ghosh, Ranadhir , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at Innovations in Applied Artificial Intelligence: 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Ottawa, Canada : 17th May, 2004
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- Description: Modular neural networks have the possibility of overcoming common scalability and interference problems experienced by fully connected neural networks when applied to large databases. In this paper we trial an approach to constructing modular ANN's for a very large problem from CEDAR for the classification of handwritten characters. In our approach, we apply progressive task decomposition methods based upon clustering and regression techniques to find modules. We then test methods for combining the modules into ensembles and compare their structural characteristics and classification performance with that of an ANN having a fully connected topology. The results reveal improvements to classification rates as well as network topologies for this problem.
- Description: E1
- Description: 2003000852
A formal description of ontology change in OWL
- Authors: Avery, John , Yearwood, John
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at the Third International Conference on Information Technology and Applications, ICITA 2005, Sydney : 4th - 7th July, 2005
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- Description: There are three main activities involved in managing ontology change. Firstly we need to identify changes, secondly describe these identified changes, and finally describe and handle the ramifications of the changes. In previous work we have presented a language (DOWL) for describing ontology change and in this paper we demonstrate how changes described in this language can be represented in the RDF abstract syntax which enables us to describe the ramifications of a change in a formal manner. This formalism can provide the basis for an automated ontology change management system.
- Description: E1
- Description: 2003001448
Applying anatomical therapeutic chemical (ATC) and critical term ontologies to Australian drug safety data for association rules and adverse event signalling
- Authors: Saunders, Gary , Ivkovic, Sasha , Ghosh, Ranadhir , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Conferences in Research and Practice in Information Technology, Advances in Ontologies 2005: Proceedings of the Australasian Ontology Workshop AOW 2005 Vol. 58, no. (2005), p. 93-98
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- Description: C1
- Description: 2003001450
Decisions surrounding adverse drug reaction prescribing : Insights from consumers and implications for decision support
- Authors: O'Brien, Michelle , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 57-71
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- Description: This paper presents findings from case studies of health consumers who each suspect they may have experienced an adverse drug reaction (ADR). These case studies are part of a larger study involving consumer/doctor decisions surrounding suspected adverse drug reactions and prescribing. Decision support to assist with the diagnosis and management of ADRs has, to date, primarily focused on providing in-time information to prescribers about factors that pertain to the consumer and the medications they are taking. Decision support that includes consumers usually targets treatment decisions. The results of this paper indicate the prescriber is only one decision contributor in a rich tapestry of decision contributors and decision types, and consumer decision types are significantly broader than treatment decisions. The results provide guidance for the development of decision support within this domain.
- Description: C1
- Description: 2003001435
Predicting Australian stock market index using neural networks exploiting dynamical swings and intermarket influences
- Authors: Pan, Heping , Tilakaratne, Chandima , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 43-55
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- Description: This paper presents a computational approach for predicting the Australian stock market index AORD using multi-layer feed-forward neural networks front the time series data of AORD and various interrelated markets. This effort aims to discover an effective neural network, or a set of adaptive neural networks for this prediction purpose, which can exploit or model various dynamical swings and inter-market influences discovered from professional technical analysis and quantitative analysis. Within a limited range defined by our empirical knowledge, three aspects of effectiveness on data selection are considered: effective inputs from the target market (AORD) itself, a sufficient set of interrelated markets,. and effective inputs from the interrelated markets. Two traditional dimensions of the neural network architecture are also considered: the optimal number of hidden layers, and the optimal number of hidden neurons for each hidden layer. Three important results were obtained: A 6-day cycle was discovered in the Australian stock market during the studied period; the time signature used as additional inputs provides useful information; and a basic neural network using six daily returns of AORD and one daily, returns of SP500 plus the day of the week as inputs exhibits up to 80% directional prediction correctness.
- Description: C1
- Description: 2003001440
Structured reasoning to support deliberative dialogue
- Authors: Macfadyen, Alyx , Stranieri, Andrew , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Lecture Notes in Artificial Intelligence 3681: Knowledge-Based Intelligent Information and Engineering Systems, 9th International Conference, KES 2005, Melbourne, Australia, September 2005, Proceedings, Part 1 Vol. 1, no. (2005), p. 283-289
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- Description: Deliberative dialogue is a form of dialogue that involves participants advancing claims and, without power plays or posturing, deliberating on the claims of others until a consensus decision is reached. This paper describes a deliberative support system to facilitate and encourage participants to engage in a discussion deliberatively. A knowledge representation framework is deployed to generate a strong domain model of reasoning structure. The structure, coupled with a deliberative dialogue protocol results in a web based system that regulates a discussion to avoid combative, non-deliberative exchanges. The system has been designed for online dispute resolution between husband and wife in divorce proceedings involving property.
- Description: C1
- Description: 2003001381
A semantic approach to boost passage retrieval effectiveness for question answering
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at Computer Science 2006 Twenty-Ninth Australian Computer Science Conference, Hobart : 16th January, 2006 p. 95-101
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- Description: In the current state of the rapid growth of information resources and the huge number of requests submitted by users to existing information retrieval systems; recently, Question Answering systems have attracted more attention to meet information needs providing users with more precise and focused retrieval units. As one of the most challenging and important processes of such systems is to retrieve the best related text excerpts with regard to the questions, we propose a novel approach to exploit not only the syntax of the natural language of the questions and texts, but also the semantics relayed beneath them via a semantic question rewriting and passage retrieval task. The semantic structure used to address the surface mismatch of the semantically related passages and queries is FrameNet which is a lexical resource for English constituted based on frame semantics. We have run our proposed approach on a subset of the TREC 2004 factoid questions to retrieve passages containing correct answers from the AQUAINT collection and we have obtained promising results.
- Description: E1
- Description: 2003001803
An interaction framework for scenario-based three dimensional environments
- Authors: Macfadyen, Alyx , Stranieri, Andrew , Yearwood, John
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at IE 2006, the 3rd Australasian Conference on Interactive Entertainment, Perth : 4th December, 2006
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- Description: Although popular and engaging, three dimensional environments are rarely deployed to depict strong narratives involving complex characters engaged in reasoning. The design of three dimensional environments rich in narrative and character depth can be facilitated with a detailed representation of interactions between characters. However, the representation of interaction in current 3D development environments such as game engines is quite basic. This work advances a scheme for representing interactions that integrates a representation of semantics from linguistics called FrameNet with conceptualizations of drama and narrative by Georges Polti and Joseph Campbell. The resulting interaction frame facilitates the design of 3D environments by providing designers rich, yet standard elements that include spatial and temporal data, with which to represent complex interactions in 3D environments. This has application for the authoring of dynamically generated interactive narrative environments.
- Description: E1
- Description: 2003001839
Using association and overlapping time window approach to detect drug reaction signals
- Authors: Ivkovic, Sasha , Saunders, Gary , Ghosh, Ranadhir , Yearwood, John
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at CIMCA 2005 International Conference on Computational Intelligence for Modelling Control & Automation jointly with IAWTIC 2005 International Conference on Intelligent Agents, Web Technologies & Internet Commerce, Vienna, Austria : 28th November, 2005 p. 1045-1053
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- Description: The problem with detecting adverse drug reactions (ADRs) from drugs is that they may not be obvious until long after they are widely prescribed. Part of the problem is these events are rare. This work describes an approach to signal detection of ADRs based on association rules (AR) in Australian drug safety data. This work was carried out using the Australian Adverse Drug Reactions Advisory Committee (ADRAC) database, which contains a hundred and thirty seven thousand records collected in 1972-2001 period. Many signal detection methods have been developed for drug safety data, most of which use a classical statistical approach. Some of these stratify the data using an ontology for reactions, but the application of drug ontologies to ADR signal detection methods has not been reported. We propose a novel approach for detecting various signal levels by using an overlapped windowing approach. The overlapping windows help to detect smooth transition of signal. We use association rules for measuring significant change over time for different hierarchical levels of drugs (using the Anatomical-Therapeutic-Chemical (ATC) system of drug classification ontology) and their reactions based on the System Organ Classes (SOC) ontology. Using association rules and their strength for different levels in the drug and reaction hierarchy, helps in the detection of signals at particular levels in higher order using a bottom up approach. The results of a preliminary investigation of ADRAC data using our method demonstrate that this approach could produce a powerful and robust ADR signal detection method.
- Description: E1
- Description: 2003001838
A fully automated CAD system using multi-category feature selection with restricted recombination
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Mukherjee, Subhasis
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 106-111
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- Description: In pattern recognition problems features plays an important role for classification results. It is very important which features are used and how many features are used for the classification process. Most of the real life classification problem uses different category of features. It is desirable to find the optimal combination of features that improves the performance of the classifier. There exists different selection framework that selects the features. Mostly do not incorporate the impact of one category of features on another. Even if they incorporate, they produce conflict between the categories. In this paper we proposed a restricted crossover selection framework which incorporate the impact of different categories on each other, as well as it restricts the search within the category which searching in the global region of the search space. The results obtained by the proposed framework are promising.
- Description: 2003005429
Classification for accuracy and insight : A weighted sum approach
- Authors: Quinn, Anthony , Stranieri, Andrew , Yearwood, John
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Sixth Australasian Data Mining Conference, AusDM 2007, Gold Coast, Queensland, Victoria : 3rd-4th December 2007 p. 203-208
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- Description: This research presents a classifier that aims to provide insight into a dataset in addition to achieving classification accuracies comparable to other algorithms. The classifier called, Automated Weighted Sum (AWSum) uses a weighted sum approach where feature values are assigned weights that are summed and compared to a threshold in order to classify an example. Though naive, this approach is scalable, achieves accurate classifications on standard datasets and also provides a degree of insight. By insight we mean that the technique provides an appreciation of the influence a feature value has on class values, relative to each other. AWSum provides a focus on the feature value space that allows the technique to identify feature values and combinations of feature values that are sensitive and important for a classification. This is particularly useful in fields such as medicine where this sort of micro-focus and understanding is critical in classification.
- Description: 2003005504