Showing items 1 - 20 of 80

Your selections:

An anti-plagiarism editor for software development courses

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
  • Full Text:
  • 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

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
  • Reviewed:
  • 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

Enhanced temporal difference learning using compiled eligibility traces

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.
  • Full Text:
  • Reviewed:
  • 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

Portal-based sound propagation for first-person computer games

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
  • Full Text:
  • 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

On the limitations of scalarisation for multi-objective reinforcement learning of Pareto fronts

Unsupervised color textured image segmentation using cluster ensembles and MRF mdel

  • Authors: Islam, Mofakharul , Yearwood, John , Vamplew, Peter
  • Date: 2008
  • Type: Text , Book chapter
  • Relation: Advances in computer and information sciences and engineering p. 323-328
  • Full Text: false
  • Reviewed:
  • Description: We propose a novel approach to implement robust unsupervised color image content understanding approach that segments a color image into its constituent parts automatically. The aim of this work is to produce precise segmentation of color images using color and texture information along with neighborhood relationships among image pixels which will provide more accuracy in segmentation. Here, unsupervised means automatic discovery of classes or clusters in images rather than generating the class or cluster descriptions from training image sets. As a whole, in this particular work, the problem we want to investigate is to implement a robust unsupervised SVFM model based color medical image segmentation tool using Cluster Ensembles and MRF model along with wavelet transforms for increasing the content sensitivity of the segmentation model. In addition, Cluster Ensemble has been utilized for introducing a robust technique for finding the number of components in an image automatically. The experimental results reveal that the proposed tool is able to find the accurate number of objects or components in a color image and eventually capable of producing more accurate and faithful segmentation and can. A statistical model based approach has been developed to estimate the Maximum a posteriori (MAP) to identify the different objects/components in a color image. The approach utilizes a Markov Random Field model to capture the relationships among the neighboring pixels and integrate that information into the Expectation Maximization (EM) model fitting MAP algorithm. The algorithm simultaneously calculates the model parameters and segments the pixels iteratively in an interleaved manner. Finally, it converges to a solution where the model parameters and pixel labels are stabilized within a specified criterion. Finally, we have compared our results with another well-known segmentation approach.

Using stereotypes to improve early-match poker play

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
  • Full Text:
  • 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

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
  • Full Text:
  • Reviewed:
  • 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.

Applying clustering and ensemble clustering approaches to phishing profiling

Constructing stochastic mixture policies for episodic multiobjective reinforcement learning tasks

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.

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.

MRF model based unsupervised color textured image segmentation using multidimensional spatially variant finite mixture model

  • Authors: Islam, Mofakharul , Vamplew, Peter , Yearwood, John
  • Date: 2009
  • Type: Text , Book chapter
  • Relation: Technological developments in Education and Automation p. 375-380
  • Full Text: false
  • Reviewed:
  • Description: We investigate and propose a novel approach to implement an unsupervised color image segmentation model that segments a color image meaningfully and partitions into its constituent parts automatically. The aim is to devise a robust unsupervised segmentation approach that can segment a color textured image more accurately. Here, color and texture information of each individual pixel along with the spatial relationship within its neighborhood have been considered for producing more accuracy in segmentation. In this particular work, the problem we want to investigate is to implement a robust unsupervised Multidimensional Spatially Variant Finite Mixture Model (MSVFMM) based color image segmentation approach using Cluster Ensembles and MRF model along with Daubechies wavelet transforms for increasing the content sensitivity of the segmentation model in order to get a better accuracy in segmentation. Here, Cluster Ensemble has been utilized as a robust automatic tool for finding the number of components in an image. The main idea behind this work is introducing a Bayesian inference based approach to estimate the Maximum a Posteriori (MAP) to identify the different objects/components in a color image. Markov Random Field (MRF) plays a crucial role in capturing the relationships among the neighboring pixels. An Expectation Maximization (EM) model fitting MAP algorithm segments the image utilizing the pixel’s color and texture features and the captured neighborhood relationships among them. The algorithm simultaneously calculates the model parameters and segments the pixels iteratively in an interleaved manner. Finally, it converges to a solution where the model parameters and pixel labels are stabilized within a specified criterion. Finally, we have compared our results with another recent segmentation approach [10], which is similar in nature. The experimental results reveal that the proposed approach is capable of producing more accurate and faithful segmentation and can be employed in different practical image content understanding applications.

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.

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
  • Reviewed:
  • 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.

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.