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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

A classification algorithm that derives weighted sum scores for insight into disease

  • Authors: Quinn, Anthony , Stranieri, Andrew , Yearwood, John , Hafen, Gaudenz
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: Paper presented at Third Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2009), Wellington, New Zealand : Vol. 97, p. 13-17
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  • Description: Data mining is often performed with datasets associated with diseases in order to increase insights that can ultimately lead to improved prevention or treatment. Classification algorithms can achieve high levels of predictive accuracy but have limited application for facilitating the insight that leads to deeper understanding of aspects of the disease. This is because the representation of knowledge that arises from classification algorithms is too opaque, too complex or too sparse to facilitate insight. Clustering, association and visualisation approaches enable greater scope for clinicians to be engaged in a way that leads to insight, however predictive accuracy is compromised or non-existent. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classification algorithm that provides accuracy comparable to other techniques whilst providing some insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. Clinicians are very familiar with weighted sum scoring scales so the internal representation is intuitive and easily understood. This paper presents results from the use of the AWSum approach with data from patients suffering from Cystic Fibrosis.

A constraint-based evolutionary learning approach to the expectation maximization for optimal estimation of the hidden Markov model for speech signal modeling

  • Authors: Huda, Shamsul , Yearwood, John , Togneri, Roberto
  • Date: 2009
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics Vol. 39, no. 1 (2009), p. 182-197
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  • Description: This paper attempts to overcome the tendency of the expectation-maximization (EM) algorithm to locate a local rather than global maximum when applied to estimate the hidden Markov model (HMM) parameters in speech signal modeling. We propose a hybrid algorithm for estimation of the HMM in automatic speech recognition (ASR) using a constraint-based evolutionary algorithm (EA) and EM, the CEL-EM. The novelty of our hybrid algorithm (CEL-EM) is that it is applicable for estimation of the constraint-based models with many constraints and large numbers of parameters (which use EM) like HMM. Two constraint-based versions of the CEL-EM with different fusion strategies have been proposed using a constraint-based EA and the EM for better estimation of HMM in ASR. The first one uses a traditional constraint-handling mechanism of EA. The other version transforms a constrained optimization problem into an unconstrained problem using Lagrange multipliers. Fusion strategies for the CEL-EM use a staged-fusion approach where EM has been plugged with the EA periodically after the execution of EA for a specific period of time to maintain the global sampling capabilities of EA in the hybrid algorithm. A variable initialization approach (VIA) has been proposed using a variable segmentation to provide a better initialization for EA in the CEL-EM. Experimental results on the TIMIT speech corpus show that CEL-EM obtains higher recognition accuracies than the traditional EM algorithm as well as a top-standard EM (VIA-EM, constructed by applying the VIA to EM). © 2008 IEEE.

A formula for multiple classifiers in data mining based on Brandt semigroups

  • Authors: Kelarev, Andrei , Yearwood, John , Mammadov, Musa
  • Date: 2009
  • Type: Text , Journal article
  • Relation: Semigroup Forum Vol. 78, no. 2 (2009), p. 293-309
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  • Description: A general approach to designing multiple classifiers represents them as a combination of several binary classifiers in order to enable correction of classification errors and increase reliability. This method is explained, for example, in Witten and Frank (Data Mining: Practical Machine Learning Tools and Techniques, 2005, Sect. 7.5). The aim of this paper is to investigate representations of this sort based on Brandt semigroups. We give a formula for the maximum number of errors of binary classifiers, which can be corrected by a multiple classifier of this type. Examples show that our formula does not carry over to larger classes of semigroups. © 2008 Springer Science+Business Media, LLC.

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.

A stochastic version of Expectation Maximization algorithm for better estimation of Hidden Markov Model

  • Authors: Huda, Shamsul , Yearwood, John , Togneri, Roberto
  • Date: 2009
  • Type: Text , Journal article
  • Relation: Pattern Recognition Letters Vol. 30, no. 14 (2009), p. 1301-1309
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  • Description: This paper attempts to overcome the local convergence problem of the Expectation Maximization (EM) based training of the Hidden Markov Model (HMM) in speech recognition. We propose a hybrid algorithm, Simulated Annealing Stochastic version of EM (SASEM), combining Simulated Annealing with EM that reformulates the HMM estimation process using a stochastic step between the EM steps and the SA. The stochastic processes of SASEM inside EM can prevent EM from converging to a local maximum and find improved estimation for HMM using the global convergence properties of SA. Experiments on the TIMIT speech corpus show that SASEM obtains higher recognition accuracies than the EM. © 2009 Elsevier B.V. All rights reserved.

An algorithm for the optimization of multiple classifers in data mining based on graphs

  • Authors: Kelarev, Andrei , Ryan, Joe , Yearwood, John
  • Date: 2009
  • Type: Text , Journal article
  • Relation: The Journal of Combinatorial Mathematics and Combinatorial Computing Vol. 71, no. (2009), p. 65-85
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  • Description: This article develops an efficient combinatorial algorithm based on labeled directed graphs and motivated by applications in data mining for designing multiple classifiers. Our method originates from the standard approach described in [37]. It defines a representation of a multiclass classifier in terms of several binary classifiers. We are using labeled graphs to introduce additional structure on the classifier. Representations of this sort are known to have serious advantages. An important property of these representations is their ability to correct errors of individual binary classifiers and produce correct combined output. For every representation like this we develop a combinatorial algorithm with quadratic running time to compute the largest number of errors of individual binary classifiers which can be corrected by the combined multiple classifier. In addition, we consider the question of optimizing the classifiers of this type and find all optimal representations for these multiple classifiers.
  • Description: 2003007563

Applying clustering and ensemble clustering approaches to phishing profiling

Can shallow semantic class information help answer passage retrieval?

  • Authors: Ofoghi, Bahadorreza , Yearwood, John
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: Paper presented at 22nd Australasian Joint Conference, AI 2009: Advances in Artificial Intelligence, Melbourne, Victoria : 1st-4th December 2009 p. 587–596
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  • Description: In this paper, the effect of using semantic class overlap evidence in enhancing the passage retrieval effectiveness of question answering (QA) systems is tested. The semantic class overlap between questions and passages is measured by evoking FrameNet semantic frames using a shallow term-lookup procedure. We use the semantic class overlap evidence in two ways: i) fusing passage scores obtained from a baseline retrieval system with those obtained from the analysis of semantic class overlap (fusion-based approach), and ii) revising the passage scoring function of the baseline system by incorporating semantic class overlap evidence (revision-based approach). Our experiments with the TREC 2004 and 2006 datasets show that the revision-based approach significantly improves the passage retrieval effectiveness of the baseline system.
  • Description: 2003007254

Cayley graphs as classifiers for data mining : The influence of asymmetries

  • Authors: Kelarev, Andrei , Ryan, Joe , Yearwood, John
  • Date: 2009
  • Type: Text , Journal article
  • Relation: Discrete Mathematics Vol. 309, no. 17 (2009), p. 5360-5369
  • Relation: http://purl.org/au-research/grants/arc/DP0211866
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  • Description: The endomorphism monoids of graphs have been actively investigated. They are convenient tools expressing asymmetries of the graphs. One of the most important classes of graphs considered in this framework is that of Cayley graphs. Our paper proposes a new method of using Cayley graphs for classification of data. We give a survey of recent results devoted to the Cayley graphs also involving their endomorphism monoids. © 2008 Elsevier B.V. All rights reserved.

Deliberative discourse and reasoning from generic argument structures

  • Authors: Yearwood, John , Stranieri, Andrew
  • Date: 2009
  • Type: Text , Journal article
  • Relation: AI and Society Vol. 23, no. 3 (2009), p. 353-377
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  • Description: In this article a dialectical model for practical reasoning within a community, based on the Generic/Actual Argument Model (GAAM) is advanced and its application to deliberative dialogue discussed. The GAAM, offers a dynamic template for structuring knowledge within a domain of discourse that is connected to and regulated by a community. The paper demonstrates how the community accepted generic argument structure acts to normatively influence both admissible reasoning and the progression of dialectical reasoning between participants. It is further demonstrated that these types of deliberation dialogues supported by the GAAM comply with criteria for normative principles for deliberation, specifically, Alexy's rules for discourse ethics and Hitchcock's Principles of Rational Mutual Inquiry. The connection of reasoning to the community in a documented and transparent structure assists in providing best justified reasons, principles of deliberation and ethical discourse which are important advantages for reasoning communities. © Springer-Verlag London Limited 2006.

Establishing phishing provenance using orthographic features

  • Authors: Liping, Ma , Yearwood, John , Watters, Paul
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: Paper presented at 2009 eCrime Researchers Summit, eCRIME '09, Tacoma, Washington : 20th-21st October 2009
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  • Description: After phishing message detection, determining the provenance of phishing messages and Websites is the second step to tracing cybercriminals. In this paper, we present a novel method to cluster phishing emails automatically using orthographic features. In particular, we develop an algorithm to cluster documents and remove redundant features at the same time. After collecting all the possible features based on observation, we adapt the modified global k-mean method repeatedly, and generate the objective function values over a range of tolerance values across different subsets of features. Finally, we identify the appropriate clusters based on studying the distribution of the objective function values. Experimental evaluation of a large number of computations demonstrates that our clustering and feature selection techniques are highly effective and achieve reliable results.
  • Description: 2003007842

Experimental investigation of three machine learning algorithms for ITS dataset

  • Authors: Yearwood, John , Kang, Byeongho , Kelarev, Andrei
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: Paper presented at First International Conference, FGIT 2009, Future Generation Information Technology, Jeju Island, Korea : 10th-12th December 2009 Vol. 5899, p. 308-316
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  • Description: The present article is devoted to experimental investigation of the performance of three machine learning algorithms for ITS dataset in their ability to achieve agreement with classes published in the biologi cal literature before. The ITS dataset consists of nuclear ribosomal DNA sequences, where rather sophisticated alignment scores have to be used as a measure of distance. These scores do not form a Minkowski metric and the sequences cannot be regarded as points in a finite dimensional space. This is why it is necessary to develop novel machine learning ap proaches to the analysis of datasets of this sort. This paper introduces a k-committees classifier and compares it with the discrete k-means and Nearest Neighbour classifiers. It turns out that all three machine learning algorithms are efficient and can be used to automate future biologically significant classifications for datasets of this kind. A simplified version of a synthetic dataset, where the k-committees classifier outperforms k-means and Nearest Neighbour classifiers, is also presented.
  • Description: 2003007844

From lexical entailment to recognizing textual entailment using linguistic resources

  • Authors: Ofoghi, Bahadorreza , Yearwood, John
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: Paper presented at Australasian Language Technology Association Workshop 2009, Sydney, New South Wales : 3rd-4th December 2009 p. 119–123
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  • Description: In this paper, we introduce our Recognizing Textual Entailment (RTE) system developed on the basis of Lexical Entailment between two text excerpts, namely the hypothesis and the text. To extract atomic parts of hypotheses and texts, we carry out syntactic parsing on the sentences. We then utilize WordNet and FrameNet lexical resources for estimating lexical coverage of the text on the hypothesis. We report the results of our RTE runs on the Text Analysis Conference RTE datasets. Using a failure analysis process, we also show that the main difficulty of our RTE system relates to the underlying difficulty of syntactic analysis of sentences.
  • Description: 2003007910

Group structured reasoning for coalescing group decisions

  • Authors: Yearwood, John , Stranieri, Andrew
  • Date: 2009
  • Type: Text , Journal article
  • Relation: Group Decision and Negotiation Vol. , no. (2009), p. 1-29
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  • Description: In this paper we present the notion of structured reasoning through a model, called the Generic/Actual Argument Model (GAAM). The model which has been used as a computational representation for machine modelling of reasoning and for hybrid combinations of human and machine reasoning can be used as a coalescent framework for decision making. Whilst the notion of structuring reasoning is not new, structured reasoning is advanced as a technique where group consensus on reasoning structures at various levels can be used to facilitate the comprehension of complex reasoning particularly where there are multiple perspectives. For an issue, the approach provides a scaffolding structure for cognitive co-operation and a normative reasoning structure against which group participants can identify points of difference and points in common as well as the nature of the differences and similarities. Intra-group transparency characterized by the ability to recognise points in common and understand the nature of differences is important to the process of coalescing group decisions that carry maximum group support. © 2009 Springer Science+Business Media B.V.

Key public sector individuals as ICT change agents : An analysis of Australian and German experience

  • Authors: Jagodick, Jana , Courvisanos, Jerry , Yearwood, John , Braun, Patrice
  • Date: 2009
  • Type: Text , Journal article
  • Relation: The Asia Pacific Journal of Public Administration Vol. 31, no. 2 (2009), p. 197-212
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  • Description: The increasing demand for technology-enabled public sector services drives state agencies to launch information and communication technology (ICT) projects. The Australian and German state agencies are taking a proactive role towards technological change by employing so-called ICT change agents. These ICT change agents introduce, diffuse, manage and implement ICT within projects. Despite the mobilisation of change agents, there is scant research on the formal and informal roles of these key individuals within public sector projects. This article bridges that gap by providing valuable insights into the activities of public sector ICT change agents. It is based on empirical research from six case studies in Australian and German state agencies. Findings from these studies indicate that public sector ICT change agents position organisations to take advantage of cutting edge technologies by performing a great variety of formal and informal roles. Formal roles are performed in order to accomplish set formal project tasks, while informal roles help to speed up rapid ICT adoption and innovation through the change agents’ informal networks. The findings are delineated in a framework for future research which shows that formal and informal roles impact on the outcomes of public sector ICT projects.
  • Description: 2003007371

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
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  • 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.

Online group deliberation for the elicitation of shared values to underpin decision making

  • Authors: Feldman, Yishai , Kraft, Donald , Kuflik, Tsvi , Afshar, Faezeh , Stranieri, Andrew , Yearwood, John
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: Paper presented at 7th International Conference, NGITS 2009, Next generation information technologies and systems, Haifa, Israel : 16th-18th June 2009 Vol. 5831, p. 158-168
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  • Description: Values have been shown to underpin our attitudes, behaviour and motivate our decisions. Values do not exist in isolation but have meaning in relation to other values. However, values are not solely the purview of individuals as communities and organisations have core values implicit in their culture, policies and practices. Values for a group can be determined by a minority in power, derived by algorithmically merging values each group member holds, or set by deliberative consensus. The elicitation of values for the group by deliberation is likely to lead to widespread acceptance of values arrived at, however enticing individuals to engage in face to face discussion about values has been found to be very difficult. We present an online deliberative communication approach for the anonymous deliberation of values and claim that the framework has the elements required for the elicitation of shared values.
  • Description: 2003007509

Optimization methods and the k-committees algorithm for clustering of sequence data

  • Authors: Yearwood, John , Bagirov, Adil , Kelarev, Andrei
  • Date: 2009
  • Type: Text , Journal article
  • Relation: Applied and Computational Mathematics Vol. 8, no. 1 (2009), p. 92-101
  • Relation: http://purl.org/au-research/grants/arc/DP0211866
  • Relation: http://purl.org/au-research/grants/arc/DP0666061
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  • Description: The present paper is devoted to new algorithms for unsupervised clustering based on the optimization approaches due to [2], [3] and [4]. We consider a novel situation, where the datasets consist of nucleotide or protein sequences and rather sophisticated biologically significant alignment scores have to be used as a measure of distance. Sequences of this kind cannot be regarded as points in a finite dimensional space. Besides, the alignment scores do not satisfy properties of Minkowski metrics. Nevertheless the optimization approaches have made it possible to introduce a new k-committees algorithm and compare its performance with previous algorithms for two datasets. Our experimental results show that the k-committees algorithms achieves intermediate accuracy for a dataset of ITS sequences, and it can perform better than the discrete k-means and Nearest Neighbour algorithms for certain datasets. All three algorithms achieve good agreement with clusters published in the biological literature before and can be used to obtain biologically significant clusterings.

Optimization of multiple classifiers in data mining based on string rewriting systems