A new loss function for robust classification
- Authors: Zhao, Lei , Mammadov, Musa , Yearwood, John
- Date: 2014
- Type: Text , Journal article
- Relation: Intelligent Data Analysis Vol. 18, no. 4 (2014), p. 697-715
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- Description: Loss function plays an important role in data classification. Manyloss functions have been proposed and applied to differentclassification problems. This paper proposes a new so called thesmoothed 0-1 loss function, that could be considered as anapproximation of the classical 0-1 loss function. Due to thenon-convexity property of the proposed loss function, globaloptimization methods are required to solve the correspondingoptimization problems. Together with the proposed loss function, wecompare the performance of several existing loss functions in theclassification of noisy data sets. In this comparison, differentoptimization problems are considered in regards to the convexity andsmoothness of different loss functions. The experimental resultsshow that the proposed smoothed 0-1 loss function works better ondata sets with noisy labels, noisy features, and outliers. © 2014 - IOS Press and the authors. All rights reserved.
Attribute weighted Naive Bayes classifier using a local optimization
- Authors: Taheri, Sona , Yearwood, John , Mammadov, Musa , Seifollahi, Sattar
- Date: 2013
- Type: Text , Journal article
- Relation: Neural Computing & Applications Vol.24, no.5 (2013), p.995-1002
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- Description: The Naive Bayes classifier is a popular classification technique for data mining and machine learning. It has been shown to be very effective on a variety of data classification problems. However, the strong assumption that all attributes are conditionally independent given the class is often violated in real-world applications. Numerous methods have been proposed in order to improve the performance of the Naive Bayes classifier by alleviating the attribute independence assumption. However, violation of the independence assumption can increase the expected error. Another alternative is assigning the weights for attributes. In this paper, we propose a novel attribute weighted Naive Bayes classifier by considering weights to the conditional probabilities. An objective function is modeled and taken into account, which is based on the structure of the Naive Bayes classifier and the attribute weights. The optimal weights are determined by a local optimization method using the quasisecant method. In the proposed approach, the Naive Bayes classifier is taken as a starting point. We report the results of numerical experiments on several real-world data sets in binary classification, which show the efficiency of the proposed method.
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
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
A hybrid neural learning algorithm using evolutionary learning and derivative free local search method
- Authors: Ghosh, Ranadhir , Yearwood, John , Ghosh, Moumita , Bagirov, Adil
- Date: 2006
- Type: Text , Journal article
- Relation: International Journal of Neural Systems Vol. 16, no. 3 (2006), p. 201-213
- Full Text: false
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- Description: In this paper we investigate a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. Also we discuss different variants for hybrid models using the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. The Discrete Gradient method has the advantage of being able to jump over many local minima and find very deep local minima. However, earlier research has shown that a good starting point for the discrete gradient method can improve the quality of the solution point. Evolutionary algorithms are best suited for global optimisation problems. Nevertheless they are cursed with longer training times and often unsuitable for real world application. For optimisation problems such as weight optimisation for ANNs in real world applications the dimensions are large and time complexity is critical. Hence the idea of a hybrid model can be a suitable option. In this paper we propose different fusion strategies for hybrid models combining the evolutionary strategy with the discrete gradient method to obtain an optimal solution much quicker. Three different fusion strategies are discussed: a linear hybrid model, an iterative hybrid model and a restricted local search hybrid model. Comparative results on a range of standard datasets are provided for different fusion hybrid models. © World Scientific Publishing Company.
- Description: C1
- Description: 2003001712
A fully automated breast cancer recognition system using discrete-gradient based clustering and multi category feature selection
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Advanced Computational Intelligence and Intelligent Informatics Vol. 9, no. 3 (2005), p. 244-256
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- Description: Advances in machine intelligence have provided a whole new window of opportunities in medical research. Building a fully automated computer aided diagnostic system for digital mammograms is just one of them. Given some success with semi-automated systems earlier, a fully automated CAD system is just another step forward. A proper combination of a feature selection model and a classifier for those areas of a mammogram marked by radiologists has been very successful. However a fully automated system with only two modules is a time consuming process as the suspicious areas in a mammogram can be quite small when compared to the whole image. Thus an additional clustering process can help in reducing the time complexity of the overall process. In this paper we propose a fast clustering process to identify suspicious areas. Another novelty of this paper is a multi-category feature selection approach. The choice of features to represent the patterns affects several aspects of pattern recognition problems such as accuracy, required learning time and the required number of samples. In this paper we propose a hybrid canonical based feature extraction technique as a combination of an evolutionary algorithm based classifier with a feed forward MLP model.
- Description: C1
- Description: 2003001358
Modular neural network design for the problem of alphabetic character recognition
- Authors: Ferguson, Brent , Ghosh, Ranadhir , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: International Journal of Pattern Recognition and Artificial Intelligence Vol. 19, no. 2 (Mar 2005), p. 249-269
- Full Text: false
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- Description: This paper reports on an experimental approach to nd a modularized articial neural network solution for the UCI letters recognition problem. Our experiments have been carried out in two parts. We investigate directed task decomposition using expert knowledge and clustering approaches to nd the subtasks for the modules of the network. We next investigate processes to combine the modules e ectively in a single decision process. After having found suitable modules through task decomposition we have found through further experimentation that when the modules are combined with decision tree supervision, their functional error is reduced signicantly to improve their combination through the decision process that has been implemented as a small multilayered perceptron. The experiments conclude with a modularized neural network design for this classication problem that has increased learning and generalization characteristics. The test results for this network are markedly better than a single or stand alone network that has a fully connected topology.
- Description: C1
- Description: 2003001355
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
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
System development a la MODDE
- Authors: Meikle, Tunde , Yearwood, John
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 8th International Conference on Artificial Intelligence and Law - ICAIL '01, St. Louis, Missouri, USA : 21st-25th May 2001 p. 99-103
- Full Text: false
- Description: This paper describes the MODDE (Model of Decision support system Design and Evaluation) framework in some detail. The work is in progress and is being currently applied to the EMBRACE project being developed for the Refugee Review Tribunal (RRT) of Australia. Refugee law is the general legal area we are working in, while the specific domain under investigation is that of the decision makers at the RRT. EMBRACE is a decision support system being designed to assist the RRT in maintaining consistency of decisions, and preserve discretion of decision makers as well as making it easier to cope with high volumes of work in decreasing time frames. The use of the MODDE framework is intended to facilitate systematic attention to important features of decision making in our specific legal domain and to provide a sound basis upon which to evaluate a part of the system intrinsic to user acceptance. Copyright 2001 ACM.
- Description: 2003003947
Tools for placing legal decision support systems on the world wide web
- Authors: Stranieri, Andrew , Yearwood, John , Zeleznikow, John
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at Eighth International Conference on Artificial Intelligence and Law, ICAIL 2001, St. Louis, USA : 21st-25th May 2001
- Full Text: false
- Description: 2003003944