Applying clustering and ensemble clustering approaches to phishing profiling
- Authors: Webb, Dean , Yearwood, John , Vamplew, Peter , Ma, Liping , Ofoghi, Bahadorreza , Kelarev, Andrei
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at Eighth Australasian Data Mining Conference, AusDM 2009, University of Melbourne, Melbourne, Victoria : 1st–4th December 2009
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- Description: 2003007911
The Combative Accretion Model-; Multiobjective Optimisation Without Explicit Pareto Ranking
- Authors: Berry, Adam , Vamplew, Peter
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at Third International Conference, EMO 2005: Evolutionary multi-criterion optimization, Guanajuato, Mexico : 9-11 March 2005 p. 77-91
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- Description: Contemporary evolutionary multiobjective optimisation techniques are becoming increasingly focussed on the notions of archiving, explicit diversity maintenance and population-based Pareto ranking to achieve good approximations of the Pareto front. While it is certainly true that these techniques have been effective, they come at a significant complexity cost that ultimately limits their application to complex problems. This paper proposes a new model that moves away from explicit population-wide Pareto ranking, abandons both complex archiving and diversity measures and incorporates a continuous accretion-based approach that is divergent from the discretely generational nature of traditional evolutionary algorithms. Results indicate that the new approach, the Combative Accretion Model (CAM), achieves markedly better approximations than NSGA across a range of well-recognised test functions. Moreover, CAM is more efficient than NSGAII with respect to the number of comparisons (by an order of magnitude), while achieving comparable, and generally preferable, fronts.
- Description: 2003002711
Portal-based sound propagation for first-person computer games
- Authors: Foale, Cameron , Vamplew, Peter
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Fourth Australiasian Conference on Interactive Entertainment, IE2007, RMIT University, Melbourne, Victoria : 3rd-5th December 2007
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- Description: First-person computer games are a popular modern video game genre. A new method is proposed, the Directional Propagation Cache, that takes adavntage of the very common portal spatial subdivision method to accelerate environmental acoustics simulation for first-person games, by caching sound propagation information between portals.
- Description: 2003004700
Weblogs for market research : Finding more relevant opinion documents using system fusion
- Authors: Osman, Deanna , Yearwood, John , Vamplew, Peter
- Date: 2009
- Type: Text , Journal article
- Relation: Online Information Review Vol. 33, no. 5 (2009), p. 873-888
- Full Text: false
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- Description: Purpose - The purpose of this paper is to examine the usefulness of fusion as a means of improving the precision of automated opinion detection. Design/methodology/approach - Five system fusion methods are proposed and tested using runs submitted by the Text REtrieval Conference (TREC) Blog06 participants as input. The methods include a voting method, an inverse rank method (IRM), a linear-normalised score method and two weighted methods that use a weighted IRM score to rank the document. Findings - Mean average precision (MAP) is used as an indicator of the performance of the runs in this study. The best system fusion method achieves a 55.5 percent higher MAP result compared with the highest MAP result of any individual run submitted by the Blog06 participants. This equates to an increase in detection of 2,398 relevant opinion documents (21 percent). Practical implications - System fusion can be used to improve upon the results achieved by existing individual opinion detection systems. On the other hand, multiple opinion detection approaches can be combined into one system and fusion used to combine the results to build in diversity. Diversity within fusion inputs can increase the improvements achieved by fusion methods. The improved output from a diverse opinion detection system will then contain a higher number of relevant documents and reduce the incidence of high-ranking non-relevant documents and low-ranking relevant documents. Originality/value - The fusion methods proposed in this study demonstrate that simple fusion of opinion detection systems can improve performance.
More effective web search using bigrams and trigrams
- Authors: Johnson, David , Malhotra, Vishy , Vamplew, Peter
- Date: 2006
- Type: Text , Journal article
- Relation: Webology Vol. 3, no. 4 (2006), p.
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- Description: This paper investigates the effectiveness of quoted bigrams and trigrams as query terms to target web search. Prior research in this area has largely focused on static corpora each containing only a few million documents, and has reported mixed (usually negative) results. We investigate the bigram/trigram extraction problem and present an extraction algorithm that shows promising results when applied to real-time web search. We also present a prototype augmented search software package that can leverage the results provided by a web search engine to assist the web searcher identify important phrases and related documents quickly. This software has received favourable feedback in a recent user survey. Copyright © 2006, David Johnson, Vishv Malhotra, & Peter Vamplew.
- Description: C1
- Description: 2003001583
Using corpus analysis to inform research into opinion detection in blogs
- Authors: Osman, Deanna , Yearwood, John , Vamplew, Peter
- 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. 65-75
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- Description: Opinion detection research relies on labeled documents for training data, either by assumptions based on the document's origin or by using human assessors to categorise the documents. In recent years, blogs have become a source for opinion identification research (TREC Blog06). This study analyses the part-of-speech proportion and the words used within various corpora, determining key differences and similarities useful when preparing for opinion identification research. The resulting comparisons between the characteristics of the various corpora is detailed and discussed. In particular, opinion bearing and non opinion Blog06 documents were found to display a high level of similarity, indicating that blog documents assessed at the document level cannot be used as training data in opinion identification research.
- Description: 2003004892
Weblogs for market research : Improving opinion detection using system fusion
- Authors: Osman, Deanna , Yearwood, John , Vamplew, Peter
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at International Conference on Service Systems and Service Management, 2008, Melbourne, Victoria : 30th June - 2nd July 2008 p. 1-6
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- Description: Searching for opinions on a specific product or service within blogs is a new frontier for market researchers. This research investigates the use of system fusion methods to improve mean average precision (MAP) results achieved by the Text REtrieval Conference (TREC) Blog06 participants and reports the improved MAP results. It is hypothesized that diversity of the inputs is vital to maximising the MAP improvements. This is shown in the improvement in MAP values achieved by some of the participantpsilas ranked lists. The growth in the number of blog authors who write valuable opinions about their life experiences has led to an unsolicited resource of opinions on products, politics and services. In 2006, TREC collected blogs and set a task of detecting opinions on given topics to their participants, reporting the results using MAP.
- Description: 2003007757
Incorporating expert advice into reinforcement learning using constructive neural networks
- Authors: Ollington, Robert , Vamplew, Peter , Swanson, John
- Date: 2009
- Type: Text , Book chapter
- Relation: Constructive Neural Networks Chapter p. 207-224
- Full Text: false
- Description: This paper presents and investigates a novel approach to using expert advice to speed up the learning performance of an agent operating within a reinforcement learning framework. This is accomplished through the use of a constructive neural network based on radial basis functions. It is demonstrated that incorporating advice from a human teacher can substantially improve the performance of a reinforcement learning agent, and that the constructive algorithm proposed is particularly effective at aiding the early performance of the agent, whilst reducing the amount of feedback required from the teacher. The use of constructive networks within a reinforcement learning context is a relatively new area of research in itself, and so this paper also provides a review of the previous work in this area, as a guide for future researchers. © 2009 Springer-Verlag Berlin Heidelberg.
A polynomial ring construction for the classification of data
- Authors: Kelarev, Andrei , Yearwood, John , Vamplew, Peter
- Date: 2009
- Type: Text , Journal article
- Relation: Bulletin of the Australian Mathematical Society Vol. 79, no. 2 (2009), p. 213-225
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- Description: Drensky and Lakatos (Lecture Notes in Computer Science, 357 (Springer, Berlin, 1989), pp. 181-188) have established a convenient property of certain ideals in polynomial quotient rings, which can now be used to determine error-correcting capabilities of combined multiple classifiers following a standard approach explained in the well-known monograph by Witten and Frank (Data Mining: Practical Machine Learning Tools and Techniques (Elsevier, Amsterdam, 2005)). We strengthen and generalise the result of Drensky and Lakatos by demonstrating that the corresponding nice property remains valid in a much larger variety of constructions and applies to more general types of ideals. Examples show that our theorems do not extend to larger classes of ring constructions and cannot be simplified or generalised.
An efficient approach to unbounded bi-objective archives : Introducing the Mak_Tree algorithm
- Authors: Vamplew, Peter , Berry, Adam
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at GECCO 2006, 8th Annual Genetic and Evolutionary Computation Conference, Seattle, USA : 8th July, 2006
- Full Text: false
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- Description: Given the prominence of elite archiving in contemporary multiobjective optimisation research and the limitations inherent in bounded population sizes, it is unusual that the vast majority of popular techniques aggressively truncate the capacity of archives and are based upon inefficient list representations. By forming better data structures and algorithms for the storage of archival members, the need for truncation is reduced and unbounded elite sets become viable. While work does exist in this vein, it is always of a general nature and significant improvements can be made in the bi-objective case. As such, this paper elucidates the unique properties of two-dimensional non-dominated sets and capitalises on these notions to develop the highly efficient and specialised bi-objective Mak_Tree algorithm. Theoretical results indicate that the specialised approach is preferable to pre-existing general techniques, while empirical analysis illustrates improved performance over both unbounded and bounded list techniques.
- Description: E1
- Description: 2003001715
Automated opinion detection : Implications of the level of agreement between human raters
- Authors: Osman, Deanna , Yearwood, John , Vamplew, Peter
- Date: 2010
- Type: Text , Journal article
- Relation: Information Processing and Management Vol. 46, no. 3 (2010), p. 331-342
- Full Text: false
- Reviewed:
- Description: The ability to agree with the TREC Blog06 opinion assessments was measured for seven human assessors and compared with the submitted results of the Blog06 participants. The assessors achieved a fair level of agreement between their assessments, although the range between the assessors was large. It is recommended that multiple assessors are used to assess opinion data, or a pre-test of assessors is completed to remove the most dissenting assessors from a pool of assessors prior to the assessment process. The possibility of inconsistent assessments in a corpus also raises concerns about training data for an automated opinion detection system (AODS), so a further recommendation is that AODS training data be assembled from a variety of sources. This paper establishes an aspirational value for an AODS by determining the level of agreement achievable by human assessors when assessing the existence of an opinion on a given topic. Knowing the level of agreement amongst humans is important because it sets an upper bound on the expected performance of AODS. While the AODSs surveyed achieved satisfactory results, none achieved a result close to the upper bound. © 2009 Elsevier Ltd. All rights reserved.
Enhanced temporal difference learning using compiled eligibility traces
- Authors: Vamplew, Peter , Ollington, Robert , Hepburn, Mark
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at Artificial Intelligence, AI 2006: Advances in Artificial Intelligence conference, Hobart, Australia : 4th December, 2006
- Full Text: false
- Reviewed:
- Description: Eligibility traces have been shown to substantially improve the convergence speed of temporal difference learning algorithms, by maintaining a record of recently experienced states. This paper presents an extension of conventional eligibility traces (compiled traces) which retain additional information about the agent’s experience within the environment. Empirical results show that compiled traces outperform conventional traces when applied to policy evaluation tasks using a tabular representation of the state values.
- Description: E1
- Description: 2003001529
Using stereotypes to improve early-match poker play
- Authors: Layton, Robert , Vamplew, Peter , Turville, Christopher
- Date: 2008
- Type: Text , Journal article
- Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 5360 LNAI, no. (1 December 2008 through 5 December 2008 2008), p. 584-593
- Full Text: false
- Description: Agent modelling is a critical aspect of many artificial intelligence systems. Many different techniques are used to learn the tendencies of another agent, though most suffer from a slow learning time. The research proposed in this paper examines stereotyping as a method to improve the learning time of poker playing agents. Poker is a difficult domain for opponent modelling due to its hidden information, stochastic elements and complex strategies. However, the literature suggests there are clusters of similar poker strategies, making it an ideal environment to test the effectiveness of stereotyping. This paper presents a method for using stereotyping in a poker bot, and shows that stereotyping improves performance in early-match play in many scenarios. © 2008 Springer Berlin Heidelberg.
On the limitations of scalarisation for multi-objective reinforcement learning of Pareto fronts
- Authors: Vamplew, Peter , Yearwood, John , Dazeley, Richard , Berry, Adam
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand : 1st-5th December 2008 Vol. 5360, p. 372-378
- Full Text: false
- Description: Multiobjective reinforcement learning (MORL) extends RL to problems with multiple conflicting objectives. This paper argues for designing MORL systems to produce a set of solutions approximating the Pareto front, and shows that the common MORL technique of scalarisation has fundamental limitations when used to find Pareto-optimal policies. The work is supported by the presentation of three new MORL benchmarks with known Pareto fronts.
- Description: 2003006504
Constructing stochastic mixture policies for episodic multiobjective reinforcement learning tasks
- Authors: Vamplew, Peter , Dazeley, Richard , Barker, Ewan , Kelarev, Andrei
- Date: 2009
- Type: Text , Book chapter
- Relation: AI 2009 : Advances in Artificial Intelligence : 22nd Australasian Joint Conference, Melbourne, Australia, December 1-4, 2009. Proceedings Chapter p. 340-349
- Full Text:
- Description: Multiobjective reinforcement learning algorithms extend reinforcement learning techniques to problems with multiple conflicting objectives. This paper discusses the advantages gained from applying stochastic policies to multiobjective tasks and examines a particular form of stochastic policy known as a mixture policy. Two methods are proposed for deriving mixture policies for episodic multiobjective tasks from deterministic base policies found via scalarised reinforcement learning. It is shown that these approaches are an efficient means of identifying solutions which offer a superior match to the user’s preferences than can be achieved by methods based strictly on deterministic policies.
- Description: 2003007906
Inference of gene expression networks using memetic gene expression programming
- Authors: Zarnegar, Armita , Vamplew, Peter , Stranieri, Andrew
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at Thirty-Second Australasian Computer Science Conference (ACSC 2009), Wellington, New Zealand : Vol. 91, p. 17-23
- Full Text:
- Description: In this paper we aim to infer a model of genetic networks from time series data of gene expression profiles by using a new gene expression programming algorithm. Gene expression networks are modelled by differential equations which represent temporal gene expression relations. Gene Expression Programming is a new extension of genetic programming. Here we combine a local search method with gene expression programming to form a memetic algorithm in order to find not only the system of differential equations but also fine tune its constant parameters. The effectiveness of the proposed method is justified by comparing its performance with that of conventional genetic programming applied to this problem in previous studies.
A survey of multi-objective sequential decision-making
- Authors: Roijers, Diederik , Vamplew, Peter , Whiteson, Shimon , Dazeley, Richard
- Date: 2013
- Type: Text , Journal article
- Relation: Journal of Artificial Intelligence Research Vol. 48, no. (2013), p. 67-113
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- Description: Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential decision-making problems with multiple objectives. Though there is a growing body of literature on this subject, little of it makes explicit under what circumstances special methods are needed to solve multi-objective problems. Therefore, we identify three distinct scenarios in which converting such a problem to a single-objective one is impossible, infeasible, or undesirable. Furthermore, we propose a taxonomy that classifies multi-objective methods according to the applicable scenario, the nature of the scalarization function (which projects multi-objective values to scalar ones), and the type of policies considered. We show how these factors determine the nature of an optimal solution, which can be a single policy, a convex hull, or a Pareto front. Using this taxonomy, we survey the literature on multi-objective methods for planning and learning. Finally, we discuss key applications of such methods and outline opportunities for future work. © 2013 AI Access Foundation.
- Description: C1
Empirical evaluation methods for multiobjective reinforcement learning algorithms
- Authors: Vamplew, Peter , Dazeley, Richard , Berry, Adam , Issabekov, Rustam , Dekker, Evan
- Date: 2011
- Type: Text , Journal article
- Relation: Machine Learning Vol. 84, no. 1-2 (2011), p. 51-80
- Full Text: false
- Reviewed:
- Description: While a number of algorithms for multiobjective reinforcement learning have been proposed, and a small number of applications developed, there has been very little rigorous empirical evaluation of the performance and limitations of these algorithms. This paper proposes standard methods for such empirical evaluation, to act as a foundation for future comparative studies. Two classes of multiobjective reinforcement learning algorithms are identified, and appropriate evaluation metrics and methodologies are proposed for each class. A suite of benchmark problems with known Pareto fronts is described, and future extensions and implementations of this benchmark suite are discussed. The utility of the proposed evaluation methods are demonstrated via an empirical comparison of two example learning algorithms. © 2010 The Author(s).
Unsupervised segmentation of Industrial Images using Markov Random Field Model
- Authors: Islam, Mofakharul , Yearwood, John , Vamplew, Peter
- Date: 2009
- Type: Text , Book chapter
- Relation: Technogical Developments in Education and Automation p. 369-374
- Full Text: false
- Reviewed:
- Description: We propose a novel approach to investigate and implement unsupervised image content understanding and segmentation of color industrial images like medical imaging, forensic imaging, security and surveillance imaging, biotechnical imaging, biometrics, mineral and mining imaging, material science imaging, and many more. In this particular work, our focus will be on medical images only. The aim is to develop a computer aided diagnosis (CAD) system based on a newly developed Multidimensional Spatially Variant Finite Mixture Model (MSVFMM) using Markov Random Fields (MRF) Model. Unsupervised means automatic discovery of classes or clusters in images rather than generating the class or cluster descriptions from training image sets. The aim of this work is to produce precise segmentation of color medical images on the basis of subtle color and texture variation. Finer segmentation of images has tremendous potential in medical imaging where subtle information related to color and texture is required to analyze the image accurately. In this particular work, we have used CIE-Luv and Daubechies wavelet transforms as color and texture descriptors respectively. Using the combined effect of a CIE-Luv color model and Daubechies transforms, we can segment color medical images precisely in a meaningful manner. The evaluation of the results is done through comparison of the segmentation quality with another similar alternative approach and it is found that the proposed approach is capable of producing more faithful segmentation.
Visualising the value of water
- Authors: Block, Jessica , Graymore, Michelle , Wallis, Anne , Vamplew, Peter , Mitchell, Bradley , O'Toole, Kevin , McRae-Williams, Pamela
- Date: 2012
- Type: Text , Book chapter
- Relation: Pipes, Ponds and People: Adaptive water management p. 195-225
- Full Text: false
- Reviewed: