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Feature selection using misclassification counts

- Bagirov, Adil, Yatsko, Andrew, Stranieri, Andrew

  • Authors: Bagirov, Adil , Yatsko, Andrew , Stranieri, Andrew
  • Date: 2011
  • Type: Conference proceedings , Unpublished work
  • Relation: Proceedings of the 9th Australasian Data Mining Conference (AusDM 2011), 51-62. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 121.
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  • Description: Dimensionality reduction of the problem space through detection and removal of variables, contributing little or not at all to classification, is able to relieve the computational load and instance acquisition effort, considering all the data attributes accessed each time around. The approach to feature selection in this paper is based on the concept of coherent accumulation of data about class centers with respect to coordinates of informative features. Ranking is done on the degree to which different variables exhibit random characteristics. The results are being verified using the Nearest Neighbor classifier. This also helps to address the feature irrelevance and redundancy, what ranking does not immediately decide. Additionally, feature ranking methods from different independent sources are called in for the direct comparison.
  • Description: Dimensionality reduction of the problem space through detection and removal of variables, contributing little or not at all to classification, is able to relieve the computational load and the data acquisition effort, considering all data components being accessed each time around. The approach to feature selection in this paper is based on the concept of coherent accumulation of data about class centers with respect to coordinates of informative features. Ranking is done on the degree, to which different variables exhibit random characteristics. The results are being verified using the Nearest Neighbor classifier. This also helps to address the feature irrelevance, what ranking does not immediately decide. Additionally, feature ranking methods available from different independent sources are called in for direct comparison.

Fast modified global k-means algorithm for incremental cluster construction

- Bagirov, Adil, Ugon, Julien, Webb, Dean

  • Authors: Bagirov, Adil , Ugon, Julien , Webb, Dean
  • Date: 2011
  • Type: Text , Journal article
  • Relation: Pattern Recognition Vol. 44, no. 4 (2011), p. 866-876
  • Relation: http://purl.org/au-research/grants/arc/DP0666061
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  • Description: The k-means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and are inefficient for solving clustering problems in large datasets. Recently, incremental approaches have been developed to resolve difficulties with the choice of starting points. The global k-means and the modified global k-means algorithms are based on such an approach. They iteratively add one cluster center at a time. Numerical experiments show that these algorithms considerably improve the k-means algorithm. However, they require storing the whole affinity matrix or computing this matrix at each iteration. This makes both algorithms time consuming and memory demanding for clustering even moderately large datasets. In this paper, a new version of the modified global k-means algorithm is proposed. We introduce an auxiliary cluster function to generate a set of starting points lying in different parts of the dataset. We exploit information gathered in previous iterations of the incremental algorithm to eliminate the need of computing or storing the whole affinity matrix and thereby to reduce computational effort and memory usage. Results of numerical experiments on six standard datasets demonstrate that the new algorithm is more efficient than the global and the modified global k-means algorithms. © 2010 Elsevier Ltd. All rights reserved.

Exploration of the trade-offs between water quality and pumping costs in optimal operation of regional multiquality water distribution systems

- Mala-Jetmarova, Helena, Barton, Andrew, Bagirov, Adil

  • Authors: Mala-Jetmarova, Helena , Barton, Andrew , Bagirov, Adil
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Journal of Water Resources Planning and Management Vol. 141, no. 6 (2015), p. 1-16
  • Relation: http://purl.org/au-research/grants/arc/LP0990908
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  • Description: This paper explores the trade-offs between water quality and pumping costs objectives in optimization of operation of regional multiquality water distribution systems. The optimization model is designed to concurrently minimize each objective, where water quality is represented by the deviations of constituent concentrations from required values and pumping costs are represented by energy consumed by the pumps. The optimization problem is solved using an optimization software, incorporating the nondominated sorting genetic algorithm II (NSGA-II), linked with network analysis software. Two typical but purposefully different example networks are used. First, a network with multiple water sources of different qualities and second, a network with one water source only, which was converted to represent a regional nondrinking water distribution system. The trade-offs between water quality and pumping costs are explored using a total of 14 scenarios reflecting different water quality configurations of these networks. Those scenarios, into which time variability was introduced for both source water quality and customer water quality requirements, were systematically developed to represent real-life situations that could be found in practice. The results indicate that for the majority of the scenarios, there is a trade-off with a competing nature between water quality and pumping costs objectives. Additionally, it was discovered that multiobjective optimization problems with water quality (i.e., concentration deviations) and pumping costs objectives could be reduced in certain instances into a single-objective problem of minimizing pumping costs. In fact, a regional water distribution system in which water quality is represented by a single conservative constituent can produce either a trade-off or single-objective solution between those two objectives, and this outcome is dependent on both the water quality configuration of the system and system operational flexibility. Last, some particular conclusions are drawn for both a water distribution system with multiple water sources and a water distribution system with a single water source, which suggest how changes in source water qualities or customer water quality requirements may impact system operation. It is, therefore, demonstrated that water utilities which operate regional multiquality nondrinking water distribution systems could benefit from the exploration of trade-offs between water quality and pumping costs for the purpose of operational planning.

Estimation of a regression function by maxima of minima of linear functions

- Bagirov, Adil, Clausen, Conny, Kohler, Michael

  • Authors: Bagirov, Adil , Clausen, Conny , Kohler, Michael
  • Date: 2009
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Information Theory Vol. 55, no. 2 (2009), p. 833-845
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  • Description: In this paper, estimation of a regression function from independent and identically distributed random variables is considered. Estimates are defined by minimization of the empirical L2 risk over a class of functions, which are defined as maxima of minima of linear functions. Results concerning the rate of convergence of the estimates are derived. In particular, it is shown that for smooth regression functions satisfying the assumption of single index models, the estimate is able to achieve (up to some logarithmic factor) the corresponding optimal one-dimensional rate of convergence. Hence, under these assumptions, the estimate is able to circumvent the so-called curse of dimensionality. The small sample behavior of the estimates is illustrated by applying them to simulated data. © 2009 IEEE.

Double bundle method for finding clarke stationary points in nonsmooth dc programming

- Joki, Kaisa, Bagirov, Adil, Karmitsa, Napsu, Makela, Marko, Taheri, Sona

  • Authors: Joki, Kaisa , Bagirov, Adil , Karmitsa, Napsu , Makela, Marko , Taheri, Sona
  • Date: 2018
  • Type: Text , Journal article
  • Relation: SIAM Journal on Optimization Vol. 28, no. 2 (2018), p. 1892-1919
  • Relation: http://purl.org/au-research/grants/arc/DP140103213
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  • Description: The aim of this paper is to introduce a new proximal double bundle method for unconstrained nonsmooth optimization, where the objective function is presented as a difference of two convex (DC) functions. The novelty in our method is a new escape procedure which enables us to guarantee approximate Clarke stationarity for solutions by utilizing the DC components of the objective function. This optimality condition is stronger than the criticality condition typically used in DC programming. Moreover, if a candidate solution is not approximate Clarke stationary, then the escape procedure returns a descent direction. With this escape procedure, we can avoid some shortcomings encountered when criticality is used. The finite termination of the double bundle method to an approximate Clarke stationary point is proved by assuming that the subdifferentials of DC components are polytopes. Finally, some encouraging numerical results are presented.

Discrete gradient methods

- Bagirov, Adil, Taheri, Sona, Karmitsa, Napsu

  • Authors: Bagirov, Adil , Taheri, Sona , Karmitsa, Napsu
  • Date: 2020
  • Type: Text , Book chapter
  • Relation: Numerical Nonsmooth Optimization: State of the Art Algorithms p. 621-654
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  • Description: In this chapter, the notion of a discrete gradient is introduced and it is shown that the discrete gradients can be used to approximate subdifferentials of a broad class of nonsmooth functions. Two methods based on such approximations, more specifically, the discrete gradient method (DGM) and its limited memory version (LDGB), are described. These methods are semi derivative-free methods for solving nonsmooth and, in general, nonconvex optimization problems. The performance of the methods is demonstrated using some academic test problems. © Springer Nature Switzerland AG 2020.

Discrete gradient method : Derivative-free method for nonsmooth optimization

- Bagirov, Adil, Karasozen, Bulent, Sezer, Monsalve

  • Authors: Bagirov, Adil , Karasozen, Bulent , Sezer, Monsalve
  • Date: 2008
  • Type: Text , Journal article
  • Relation: Journal of Optimization Theory and Applications Vol. 137, no. 2 (2008), p. 317-334
  • Relation: http://purl.org/au-research/grants/arc/DP0666061
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  • Description: A new derivative-free method is developed for solving unconstrained nonsmooth optimization problems. This method is based on the notion of a discrete gradient. It is demonstrated that the discrete gradients can be used to approximate subgradients of a broad class of nonsmooth functions. It is also shown that the discrete gradients can be applied to find descent directions of nonsmooth functions. The preliminary results of numerical experiments with unconstrained nonsmooth optimization problems as well as the comparison of the proposed method with the nonsmooth optimization solver DNLP from CONOPT-GAMS and the derivative-free optimization solver CONDOR are presented. © 2007 Springer Science+Business Media, LLC.
  • Description: C1

Diagnostic with incomplete nominal/discrete data

- Jelinek, Herbert, Yatsko, Andrew, Stranieri, Andrew, Venkatraman, Sitalakshmi, Bagirov, Adil

  • Authors: Jelinek, Herbert , Yatsko, Andrew , Stranieri, Andrew , Venkatraman, Sitalakshmi , Bagirov, Adil
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Artificial Intelligence Research Vol. 4, no. 1 (2015), p. 22-35
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  • Description: Missing values may be present in data without undermining its use for diagnostic / classification purposes but compromise application of readily available software. Surrogate entries can remedy the situation, although the outcome is generally unknown. Discretization of continuous attributes renders all data nominal and is helpful in dealing with missing values; particularly, no special handling is required for different attribute types. A number of classifiers exist or can be reformulated for this representation. Some classifiers can be reinvented as data completion methods. In this work the Decision Tree, Nearest Neighbour, and Naive Bayesian methods are demonstrated to have the required aptness. An approach is implemented whereby the entered missing values are not necessarily a close match of the true data; however, they intend to cause the least hindrance for classification. The proposed techniques find their application particularly in medical diagnostics. Where clinical data represents a number of related conditions, taking Cartesian product of class values of the underlying sub-problems allows narrowing down of the selection of missing value substitutes. Real-world data examples, some publically available, are enlisted for testing. The proposed and benchmark methods are compared by classifying the data before and after missing value imputation, indicating a significant improvement.

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

Derivative-free methods for non-smooth optimization

- Bagirov, Adil

Derivative free stochastic discrete gradient method with adaptive mutation

- Ghosh, Ranadhir, Ghosh, Moumita, Bagirov, Adil

  • Authors: Ghosh, Ranadhir , Ghosh, Moumita , Bagirov, Adil
  • Date: 2006
  • Type: Text , Journal article
  • Relation: Advances in Data Mining Vol. 4065, no. (2006), p. 264-278
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  • Description: In data mining we come across many problems such as function optimization problem or parameter estimation problem for classifiers for which a good learning algorithm for searching is very much necessary. In this paper we propose a stochastic based derivative free algorithm for unconstrained optimization problem. Many derivative-based local search methods exist which usually stuck into local solution for non-convex optimization problems. On the other hand global search methods are very time consuming and works for only limited number of variables. In this paper we investigate a derivative free multi search gradient based method which overcomes the problems of local minima and produces global solution in less time. We have tested the proposed method on many benchmark dataset in literature and compared the results with other existing algorithms. The results are very promising.
  • Description: C1
  • Description: 2003001541

DC programming algorithm for clusterwise linear L1 regression

- Bagirov, Adil, Taheri, Sona

  • Authors: Bagirov, Adil , Taheri, Sona
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Journal of the Operations Research Society of China Vol. 5, no. 2 (2017), p. 233-256
  • Relation: http://purl.org/au-research/grants/arc/DP140103213
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  • Description: The aim of this paper is to develop an algorithm for solving the clusterwise linear least absolute deviations regression problem. This problem is formulated as a nonsmooth nonconvex optimization problem, and the objective function is represented as a difference of convex functions. Optimality conditions are derived by using this representation. An algorithm is designed based on the difference of convex representation and an incremental approach. The proposed algorithm is tested using small to large artificial and real-world data sets. © 2017, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag Berlin Heidelberg.

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

Cyberattack triage using incremental clustering for intrusion detection systems

- Taheri, Sona, Bagirov, Adil, Gondal, Iqbal, Brown, Simon

  • Authors: Taheri, Sona , Bagirov, Adil , Gondal, Iqbal , Brown, Simon
  • Date: 2020
  • Type: Text , Journal article
  • Relation: International Journal of Information Security Vol. 19, no. 5 (2020), p. 597-607
  • Relation: http://purl.org/au-research/grants/arc/DP190100580
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  • Description: Intrusion detection systems (IDSs) are devices or software applications that monitor networks or systems for malicious activities and signals alerts/alarms when such activity is discovered. However, an IDS may generate many false alerts which affect its accuracy. In this paper, we develop a cyberattack triage algorithm to detect these alerts (so-called outliers). The proposed algorithm is designed using the clustering, optimization and distance-based approaches. An optimization-based incremental clustering algorithm is proposed to find clusters of different types of cyberattacks. Using a special procedure, a set of clusters is divided into two subsets: normal and stable clusters. Then, outliers are found among stable clusters using an average distance between centroids of normal clusters. The proposed algorithm is evaluated using the well-known IDS data sets—Knowledge Discovery and Data mining Cup 1999 and UNSW-NB15—and compared with some other existing algorithms. Results show that the proposed algorithm has a high detection accuracy and its false negative rate is very low. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
  • Description: This research was conducted in Internet Commerce Security Laboratory (ICSL) funded by Westpac Banking Corporation Australia. In addition, the research by Dr. Sona Taheri and A/Prof. Adil Bagirov was supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (DP190100580).

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

CR-Modified SOM to the problem of handwritten digits recognition

- Mohebi, Ehsan, Bagirov, Adil

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

Continuous approximations to subdifferentials

- Bagirov, Adil

Constrained self organizing maps for data clusters visualization

- Mohebi, Ehsan, Bagirov, Adil

  • Authors: Mohebi, Ehsan , Bagirov, Adil
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Neural Processing Letters Vol. 43, no. 3 (2016), p. 849-869
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  • Description: High dimensional data visualization is one of the main tasks in the field of data mining and pattern recognition. The self organizing maps (SOM) is one of the topology visualizing tool that contains a set of neurons that gradually adapt to input data space by competitive learning and form clusters. The topology preservation of the SOM strongly depends on the learning process. Due to this limitation one cannot guarantee the convergence of the SOM in data sets with clusters of arbitrary shape. In this paper, we introduce Constrained SOM (CSOM), the new version of the SOM by modifying the learning algorithm. The idea is to introduce an adaptive constraint parameter to the learning process to improve the topology preservation and mapping quality of the basic SOM. The computational complexity of the CSOM is less than those with the SOM. The proposed algorithm is compared with similar topology preservation algorithms and the numerical results on eight small to large real-world data sets demonstrate the efficiency of the proposed algorithm. © 2015, Springer Science+Business Media New York.

Comparison of metaheuristic algorithms for pump operation optimization

- Bagirov, Adil, Ahmed, S. T., Barton, Andrew, Mala-Jetmarova, Helena, Al Nuaimat, Alia, Sultanova, Nargiz

  • Authors: Bagirov, Adil , Ahmed, S. T. , Barton, Andrew , Mala-Jetmarova, Helena , Al Nuaimat, Alia , Sultanova, Nargiz
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: 14th Water Distribution Systems Analysis Conference 2012, WDSA 2012 Vol. 2; Adelaide, Australia; 24th-27th September 2012; p. 886-896
  • Relation: http://purl.org/au-research/grants/arc/LP0990908
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  • Description: Pumping cost constitutes the main part of the overall operating cost of water distribution systems. There are different optimization formulations of the pumping cost minimization problem including those with application of continuous and integer programming approaches. To date mainly various metaheuristics have been applied to solve this problem. However, the comprehensive comparison of those metaheuristics has not been done. Such a comparison is important to identify strengths and weaknesses of different algorithms which reflects on their performance. In this paper, we present a methodology for comparative analysis of widely used metaheuristics for solving the pumping cost minimization problem. This methodology includes the following comparison criteria: (a) the "optimal solution" obtained; (b) the efficiency; and (c) robustness. Algorithms applied are: particle swarm optimization, artificial bee colony and firefly algorithms. These algorithms were applied to one test problem available in the literature. The results obtained demonstrate that the artificial bee colony is the most robust and the firefly is the most efficient and accurate algorithm for this test problem. Funding :ARC