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
A within-frame ontological extension on FrameNet : Application in predicate chain analysis and question answering
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 20th Australian Joint Conference on Artificial Intelligence, AI 2007: Advances in Artificial Intelligence, Gold Coast, Queensland : 2nd-6th December 2007 p. 404-414
- Full Text: false
- Description: An ontological extension on the frames in FrameNet is presented in this paper. The general conceptual relations between frame elements, in conjunction with existing characteristics of this lexical resource, suggest more sophisticated semantic analysis of lexical chains (e.g. predicate chains) exploited in many text understanding applications. In particular, we have investigated its benefit for meaning-aware question answering when combined with an inference strategy. The proposed knowledge representation mechanism on the frame elements of FrameNet has been shown to have an impact on answering natural language questions on the basis of our case analysis.
- Description: 2003005507
A hybrid question answering schema using encapsulated semantics in lexical resources
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at Artificial Intelligence, AI 2006: Advances in Artificial Intelligence, Hobart : 4th December, 2006 p. 1276-1280
- Full Text: false
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- Description: E1
- Description: 2003001531
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
A variable initialization approach to the EM algorithm for better estimation of the parameters of hidden Markov Model based acoustic modeling of speech signals
- Authors: Huda, Shamsul , Ghosh, Ranadhir , Yearwood, John
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at Artificial Intelligence, Advances in Data Mining, Applications in Medicine, Web Mining, Marketing, Image and Signal Mining Conference 2006, Leipzig, Germany : 14th July, 2006 p. 416-430
- Full Text: false
- Reviewed:
- Description: The traditional method for estimation of the parameters of Hidden Markov Model (HMM) based acoustic modeling of speech uses the Expectation-Maximization (EM) algorithm. The EM algorithm is sensitive to initial values of HMM parameters and is likely to terminate at a local maximum of likelihood function resulting in non-optimized estimation for HMM and lower recognition accuracy. In this paper, to obtain better estimation for HMM and higher recognition accuracy, several candidate HMMs are created by applying EM on multiple initial models. The best HMM is chosen from the candidate HMMs which has highest value for likelihood function. Initial models are created by varying maximum frame number in the segmentation step of HMM initialization process. A binary search is applied while creating the initial models. The proposed method has been tested on TIMIT database. Experimental results show that our approach obtains improved values for likelihood function and improved recognition accuracy.
- Description: E1
- Description: 2003001542
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 CAD system using clustering and novel feature extraction technique
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at CISTM 2005, Gurgaon, India : 24th - 26th July, 2005
- Full Text: false
- Reviewed:
- Description: Many previous efforts have utilized many different approaches for recognition in breast cancer detection using various ANN classifier-modelling techniques. Most of the previous work was concentred mostly on the classification of the damaged areas with the help of doctor’s suggestion. Doctors use to mark the suspicious areas area in the mammogram and the classifier only extract those marked areas and tries to classify it. An intelligent automatic diagnosis system can be very helpful for radiologist in diagnosing Breast cancer. In this research we are applying a local search gradient free clustering algorithm to find out the suspicious / damaged area. We compare our results with the doctor’s marking. Also 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 problems such as accuracy, required learning time and a 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 solution the search space can be divided based on an individual category in each sub region and finally merging them through decision spport system. Combining the feature selection with the classifier has been a major challenge for the researchers. A similar technique employed in both the levels often worsens their performance. Some preliminary studies has revealed that while using traditional canonical GA has been a good choice for feature selection modules, however under perform for the classifier level module. An evolutionary based algorithm for the classifier level provides a much better solution for this purpose. In this paper we propose a hybrid canonical based feature extraction technique with a combination of evolutionary algorithm based classifier using a feed forward MLP model.
- Description: E1
- Description: 2003001369
A Hybrid algorithm for estimation of the parameters of Hidden Markov Model based acoustic modeling of speech signals using constraint-based genetic algorithm and expectation maximization
- Authors: Ghosh, Ranadhir , Huda, Shamsul , Yearwood, John
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at the Workshop in Learning Algorithms for Pattern Recognition, in conjunction with the 18th Australian Joint Conference on Artificial Intelligence, Sydney : 5th December, 2005
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003001368
A hybrid clustering algorithm using two level of abstraction
- Authors: Ghosh, Ranadhir , Mammadov, Musa , Ghosh, Moumita , Yearwood, John
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at Fuzzy Logic, Soft Computing, and Computational Intelligence, 11th International Fuzzy Systems Association World Congress, Beijing, China : 28th - 31st July, 2005
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003001360
Comparative analysis of genetic algorithm vs. evolutionary algorithm for hybrid models with discrete gradient method for artificial neural network
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Bagirov, Adil
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at the 11th International Fuzzy Systems Associations World Congress, IFSA 2005, Beijing, China, Volume III, Beijing, China : 28th - 31th July, 2005
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003001359
Determining regularization parameters for derivative free neural learning
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Bagirov, Adil
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at 4th International Conference, MLDM 2005: Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany : 9th-11th July 2005 p. 71-79
- Full Text: false
- Description: Derivative free optimization methods have recently gained a lot of attractions for neural learning. The curse of dimensionality for the neural learning problem makes local optimization methods very attractive; however the error surface contains many local minima. Discrete gradient method is a special case of derivative free methods based on bundle methods and has the ability to jump over many local minima. There are two types of problems that are associated with this when local optimization methods are used for neural learning. The first type of problems is initial sensitivity dependence problem- that is commonly solved by using a hybrid model. Our early research has shown that discrete gradient method combining with other global methods such as evolutionary algorithm makes them even more attractive. These types of hybrid models have been studied by other researchers also. Another less mentioned problem is the problem of large weight values for the synaptic connections of the network. Large synaptic weight values often lead to the problem of paralysis and convergence problem especially when a hybrid model is used for fine tuning the learning task. In this paper we study and analyse the effect of different regularization parameters for our objective function to restrict the weight values without compromising the classification accuracy.
- Description: 2003001362
Fusion strategies for neural learning algorithms using evolutionary and discrete gradient approaches
- Authors: Ghosh, Ranadhir , Yearwood, John , Ghosh, Moumita , Bagirov, Adil
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at AIA 2005: International Conference on Artificial Intelligence and Applications, Innsbruck, Austria : 14th - 16th February, 2006
- Full Text: false
- Reviewed:
- Description: In this paper we investigate 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.
- Description: E1
- Description: 2003001365
On recognition of handwritten devanagari numerals
- Authors: Ghosh, Ranadhir , Bhattacharya, Ujjwal , Chaudhuri, Bidyut , Ghosh, Moumita
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at the Workshop in Learning Algorithms for Pattern Recognition, in conjunction with the 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia : 5th December, 2005
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003001371
On some special properties of a neural hybrid learning method
- Authors: Ghosh, Ranadhir
- Date: 2005
- Type: Text , Conference paper
- Relation: Paper presented at CISTM 2005, Gurgaon, India : 24th - 26th July, 2005
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003001370
A feature extraction technique for online handwriting recognition
- Authors: Verma, Brijesh , Lu, Jenny , Ghosh, Moumita , Ghosh, Ranadhir
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at 2004 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary : 25th July, 2004
- Full Text: false
- Reviewed:
- Description: The paper presents a feature extraction technique for online handwriting recognition. The technique incorporates many characteristics of handwritten characters based on structural, directional and zoning information and combines them to create a single global feature vector. The technique is independent to character size and it can extract features from the raw data without resizing. Using the proposed technique and a Neural Network based classifier, many experiments were conducted on UNIPEN benchmark database. The recognition rates are 98.2% for digits, 91.2% for uppercase and 91.4% for lowercase.
- Description: E1
- Description: 2003000868
A hybrid approach for feature and architecture selection in online handwriting recognition
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at RASC 2004: Fifth International Conference on Recent Advances in Soft Computing, Nottingham, United Kingdom : 16th - 18th December, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000870
A hybrid evolutionary algorithm for multi category feature selection in breast cancer recognition
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at the Second International Conference on Software Computing and Intelligent Systems, Yokahama, Japan : 21st - 22nd September, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000869
A hybrid neural learning algorithm combining evolutionary algorithm with discrete gradient method
- Authors: Ghosh, Ranadhir , Yearwood, John , Bagirov, Adil
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at the Second International Conference on Software Computing and Intelligent Systems, Yokahama, Japan : 21st October, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000860
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
- Full Text:
- Reviewed:
- 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
A rule based plagiarism detection using decision tree
- Authors: Ghosh, Moumita , Ghosh, Ranadhir , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at the CIMCA 2004: International Conference on Computational Intelligence for Modelling, Control and Automation, Gold Coast, Queensland : 12th July, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000858