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Comparative analysis of genetic algorithm, simulated annealing and cutting angle method for artificial neural networks

- Ghosh, Ranadhir, Ghosh, Moumita, Yearwood, John, Bagirov, Adil

  • Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Bagirov, Adil
  • Date: 2005
  • Type: Text , Journal article
  • Relation: Machine Learning and Data Mining in Pattern Recognition, Proceedings Vol. 3587, no. (2005), p. 62-70
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  • Description: Neural network learning is the main essence of ANN. There are many problems associated with the multiple local minima in neural networks. Global optimization methods are capable of finding global optimal solution. In this paper we investigate and present a comparative study for the effects of probabilistic and deterministic global search method for artificial neural network using fully connected feed forward multi-layered perceptron architecture. We investigate two probabilistic global search method namely Genetic algorithm and Simulated annealing method and a deterministic cutting angle method to find weights in neural network. Experiments were carried out on UCI benchmark dataset.
  • Description: C1
  • Description: 2003003398

Data mining with combined use of optimization techniques and self-organizing maps for improving risk grouping rules : Application to prostate cancer patients

- Churilov, Leonid, Bagirov, Adil, Schwartz, Daniel, Smith, Kate, Dally, Michael

  • Authors: Churilov, Leonid , Bagirov, Adil , Schwartz, Daniel , Smith, Kate , Dally, Michael
  • Date: 2005
  • Type: Text , Journal article
  • Relation: Journal of Management Information Systems Vol. 21, no. 4 (2005), p. 85-100
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  • Description: Data mining techniques provide a popular and powerful tool set to generate various data-driven classification systems. In this paper, we investigate the combined use of self-organizing maps (SOM) and nonsmooth nonconvex optimization techniques in order to produce a working case of a data-driven risk classification system. The optimization approach strengthens the validity of SOM results, and the improved classification system increases both the quality of prediction and the homogeneity within the risk groups. Accurate classification of prostate cancer patients into risk groups is important to assist in the identification of appropriate treatment paths. We start with the existing rules and aim to improve classification accuracy by identifying inconsistencies utilizing self-organizing maps as a data visualization tool. Then, we progress to the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. Using the case of prostate cancer patients grouping, we demonstrate strong potential of data-driven risk classification schemes for addressing the risk grouping issues in more general organizational settings. © 2005 M.E. Sharpe, Inc.
  • Description: C1
  • Description: 2003001265

Determining regularization parameters for derivative free neural learning

- Ghosh, Ranadhir, Ghosh, Moumita, Yearwood, John, Bagirov, Adil

  • 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
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  • 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

- Ghosh, Ranadhir, Yearwood, John, Ghosh, Moumita, Bagirov, Adil

  • 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
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  • 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

Global optimization in the summarization of text documents

- Alyguliev, R. M., Bagirov, Adil

  • Authors: Alyguliev, R. M. , Bagirov, Adil
  • Date: 2005
  • Type: Text , Journal article
  • Relation: Automatic Control and Computer Sciences Vol. 39, no. 6 (2005), p. 42-47
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  • Description: In order to ensure minimal redundancy in the summary of a document and the greatest possible coverage of its content a method for the construction of summaries (summarization) based on the clustering of sentences is proposed in the article. Clustering of sentences reduces to a determination of cluster centroids the mathematical realization of which relies on a problem of global optimization. A determination of the number of clusters is one of the complex problems in the clustering procedure. Therefore, an algorithm of stepwise determination of the number of clusters is also proposed in the present study. © 2006 by Allerton Press, Inc.
  • Description: C1

Hybridization of neural learning algorithms using evolutionary and discrete gradient approaches

- Ghosh, Ranadhir, Yearwood, John, Ghosh, Moumita, Bagirov, Adil

  • Authors: Ghosh, Ranadhir , Yearwood, John , Ghosh, Moumita , Bagirov, Adil
  • Date: 2005
  • Type: Text , Journal article
  • Relation: Journal of Computer Science Vol. 1, no. 3 (2005), p. 387-394
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  • Description: In this study we investigated 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 study 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: C1
  • Description: 2003001357

Local optimization method with global multidimensional search

- Bagirov, Adil, Rubinov, Alex, Zhang, Jiapu

  • Authors: Bagirov, Adil , Rubinov, Alex , Zhang, Jiapu
  • Date: 2005
  • Type: Text , Journal article
  • Relation: Journal of Global Optimization Vol. 32, no. 2 (2005), p. 161-179
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  • Description: This paper presents a new method for solving global optimization problems. We use a local technique based on the notion of discrete gradients for finding a cone of descent directions and then we use a global cutting angle algorithm for finding global minimum within the intersection of the cone and the feasible region. We present results of numerical experiments with well-known test problems and with the so-called cluster function. These results confirm that the proposed algorithms allows one to find a global minimizer or at least a deep local minimizer of a function with a huge amount of shallow local minima. © Springer 2005.
  • Description: C1
  • Description: 2003001351

Max-min separability

- Bagirov, Adil

  • Authors: Bagirov, Adil
  • Date: 2005
  • Type: Text , Journal article
  • Relation: Optimization Methods and Software Vol. 20, no. 2-3 (2005), p. 271-290
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  • Description: We consider the problem of discriminating two finite point sets in the n-dimensional space by a finite number of hyperplanes generating a piecewise linear function. If the intersection of these sets is empty, then they can be strictly separated by a max-min of linear functions. An error function is introduced. This function is nonconvex piecewise linear. We discuss an algorithm for its minimization. The results of numerical experiments using some real-world datasets are presented, which show the effectiveness of the proposed approach.
  • Description: C1
  • Description: 2003001350

Supervised data classification via max-min separability

- Ugon, Julien, Bagirov, Adil

A hybrid neural learning algorithm combining evolutionary algorithm with discrete gradient method

- Ghosh, Ranadhir, Yearwood, John, Bagirov, Adil

Improving risk grouping rules for prostate cancer patients with optimization

- Churilov, Leonid, Bagirov, Adil, Schwartz, Daniel, Smith, Kate, Dally, Michael

  • Authors: Churilov, Leonid , Bagirov, Adil , Schwartz, Daniel , Smith, Kate , Dally, Michael
  • Date: 2004
  • Type: Text , Conference paper
  • Relation: Paper presented at the 37th Annual Hawaii International Conference on System Sciences, Big Island, Hawaii : 5th January, 2004
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  • Description: Data mining techniques provide a popular and powerful toolset to address both clinical and management issues in the area of health care. This paper describes the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. The cluster analysis based model is suggested and an application of non-smooth non-convex optimization techniques to solve this model is discussed. It is demonstrated that using the optimization based approach to data mining of a prostate cancer patients database can lead to generation of a significant amount of new knowledge that can be effectively utilized to enhance clinical decision making.
  • Description: E1
  • Description: 2003000846

Optimization of feed forward MLPs using the discrete gradient method

- Bagirov, Adil, Yearwood, John, Ghosh, Ranadhir

Solving Euclidian travelling salesman problem using discrete-gradient based clustering and kohonen neural network

- Ghosh, Moumita, Ugon, Julien, Ghosh, Ranadhir, Bagirov, Adil

An algorithm for clustering based on non-smooth optimization techniques

- Bagirov, Adil, Rubinov, Alex, Sukhorukova, Nadezda, Yearwood, John

An optimization-based approach to patient grouping for acute healthcare in Australia

- Bagirov, Adil, Churilov, Leonid

  • Authors: Bagirov, Adil , Churilov, Leonid
  • Date: 2003
  • Type: Text , Conference paper
  • Relation: Paper presented at Computational Science - ICCS 2003 Conference, Melbourne : 2nd June, 2003
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  • Description: The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization, and an algorithm for solving the cluster analysis problem based on the nonsmooth optimization techniques is developed. The issues of applying this algorithm to large data sets are discussed and a feature selection procedure is demonstrated. The algorithm is then applied to a hospital data set to generate new knowledge about different patterns of patients resource consumption.
  • Description: E1
  • Description: 2003000434

Comparative analysis of the cutting angle and simulated annealing methods in global optimization

- Bagirov, Adil, Zhang, Jiapu

  • Authors: Bagirov, Adil , Zhang, Jiapu
  • Date: 2003
  • Type: Text , Journal article
  • Relation: Optimization Vol. 52, no. 4-5 (2003), p. 363-378
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  • Description: This article presents a comparative analysis of two methods of global optimization: the simulated annealing method and a method based on a combination of the cutting angle method and a local search. This analysis is carried out using results of numerical experiments. These results demonstrate that the combined method is more effective than the simulated annealing method.
  • Description: C1
  • Description: 2003000436

Continuous subdifferential approximations and their applications

- Bagirov, Adil

  • Authors: Bagirov, Adil
  • Date: 2003
  • Type: Text , Journal article
  • Relation: Journal of Mathematical Sciences Vol. 115, no. 5 (2003), p. 2567-2609
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  • Description: In this paper, we study continuous approximations to the Clarke subdifferential and the Demyanov– Rubinov quasidifferential. Different methods have been proposed and discussed for the construction of the continuous approximations. Numerical methods for minimization of the locally Lipschitzian functions which are based on the continuous approximations are described and their convergence is studied. To test the proposed methods, numerical experiments have been carried out and discussed in the paper.
  • Description: C1
  • Description: 2003000423

Cutting angle method and a local search

- Bagirov, Adil, Rubinov, Alex

  • Authors: Bagirov, Adil , Rubinov, Alex
  • Date: 2003
  • Type: Text , Journal article
  • Relation: Journal of Global Optimization Vol. 27, no. 2-3 (Nov 2003), p. 193-213
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  • Description: The paper deals with combinations of the cutting angle method in global optimization and a local search. We propose to use special transformed objective functions for each intermediate use of the cutting angle method. We report results of numerical experiments which demonstrate that the proposed approach is very beneficial in the search for a global minimum.
  • Description: C1
  • Description: 2003000438

Lagrange-type functions in constrained optimization

- Rubinov, Alex, Yang, Xiao, Bagirov, Adil, Gasimov, Rafail

  • Authors: Rubinov, Alex , Yang, Xiao , Bagirov, Adil , Gasimov, Rafail
  • Date: 2003
  • Type: Text , Journal article
  • Relation: Journal of Mathematical Sciences Vol. 115, no. 4 (2003), p. 2437-2505
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  • Description: We examine various kinds of nonlinear Lagrange-type functions for constrained optimization problems. In particular, we study the weak duality, the zero duality gap property, and the existence of an exact parameter for these functions. The paper contains a detailed survey of results in these directions and comparison of different methods proposed by different authors. Some new results are also given.
  • Description: C1
  • Description: 2003000358

New algorithms for multi-class cancer diagnosis using tumor gene expression signatures

- Bagirov, Adil, Ferguson, Brent, Ivkovic, Sasha, Saunders, Gary, Yearwood, John

  • Authors: Bagirov, Adil , Ferguson, Brent , Ivkovic, Sasha , Saunders, Gary , Yearwood, John
  • Date: 2003
  • Type: Text , Journal article
  • Relation: Bioinformatics Vol. 19, no. 14 (2003), p. 1800-1807
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  • Description: Motivation: The increasing use of DNA microarray-based tumor gene expression profiles for cancer diagnosis requires mathematical methods with high accuracy for solving clustering, feature selection and classification problems of gene expression data. Results: New algorithms are developed for solving clustering, feature selection and classification problems of gene expression data. The clustering algorithm is based on optimization techniques and allows the calculation of clusters step-by-step. This approach allows us to find as many clusters as a data set contains with respect to some tolerance. Feature selection is crucial for a gene expression database. Our feature selection algorithm is based on calculating overlaps of different genes. The database used, contains over 16 000 genes and this number is considerably reduced by feature selection. We propose a classification algorithm where each tissue sample is considered as the center of a cluster which is a ball. The results of numerical experiments confirm that the classification algorithm in combination with the feature selection algorithm perform slightly better than the published results for multi-class classifiers based on support vector machines for this data set.
  • Description: C1
  • Description: 2003000439