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Target learning : A novel framework to mine significant dependencies for unlabeled data

- Wang, Limin, Chen, Shenglei, Mammadov, Musa

  • Authors: Wang, Limin , Chen, Shenglei , Mammadov, Musa
  • Date: 2018
  • Subjects: Bayesian network , Target learning , Unlabeled data
  • Type: Text , Conference proceedings , Conference Paper
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/166108 , vital:13403 , https://doi.org/10.1007/978-3-319-93034-3_9 , ISBN:03029743 (ISSN); 9783319930336 (ISBN)
  • Description: To mine significant dependencies among predictiveattributes, much work has been carried out to learn Bayesian netwrok classifiers (BNC T s) from labeled training data set T. However, if BNC T does not capture the “right” dependencies that would be most relevant to unlabeled testing instance, that will result in performance degradation. To address this issue we propose a novel framework, called target learning, that takes each unlabeled testing instance as a target and builds an “unstable” Bayesian model BNC P for it. To make BNC P and BNC T complementary to each other and work efficiently in combination, the same learning strategy is applied to build them. Experimental comparison on 32 large data sets from UCI machine learning repository shows that, for BNCs with different degrees of dependence target learning always helps improve the generalization performance with minimal additional computation. , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Optimality conditions via weak subdifferentials in reflexive Banach spaces

- Hassani, Sara, Mammadov, Musa, Jamshidi, Mina

  • Authors: Hassani, Sara , Mammadov, Musa , Jamshidi, Mina
  • Date: 2017
  • Subjects: 0101 Pure Mathematics , Supporting cone , Weak subdifferential , Nonconvex optimization
  • Type: Text , Journal article
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/155253 , vital:11260 , http://doi.org/10.3906/mat-1505-38 , ISSN:1300-0098
  • Description: In this paper the relation between the weak subdifferentials and the directional derivatives, as well as optimality conditions for nonconvex optimization problems in reflexive Banach spaces, are investigated. It partly generalizes several related results obtained for finite dimensional spaces. © Tübitak.
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A generalization of a theorem of Arrow, Barankin and Blackwell to a nonconvex case

- Kasimbeyli, Nergiz, Kasimbeyli, Refail, Mammadov, Musa

A new reliability analysis method based on the conjugate gradient direction

- Ezzati, Ghasem, Mammadov, Musa, Kulkarni, Siddhivinayak

  • Authors: Ezzati, Ghasem , Mammadov, Musa , Kulkarni, Siddhivinayak
  • Date: 2015
  • Subjects: 01 Mathematical Sciences , 09 Engineering , Conjugate gradient method , Reliability analysis , Reliability-based design optimization
  • Type: Text , Journal article
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160663 , vital:12212 , https://doi.org/10.1007/s00158-014-1113-z , ISBN:1615-147X
  • Description: Reliability-based design optimization (RBDO) is an important area in structural optimization. A principal step of the RBDO process is to solve a reliability analysis problem. This problem has been considered in inner loop of double-loop RBDO approaches. Although many algorithms have been developed for solving this problem, there are still some challenges. Existing algorithms do not have good convergence rates and often diverge. There is a need to develop more efficient and stable algorithms that can be used for evaluating all performance functions sufficiently. In this paper, a new method, called “Conjugate Gradient Analysis (CGA) Method”, is proposed to apply in the reliability analysis problems. This method is based on the conjugate gradient method. Some mathematical problems are provided in order to demonstrate the advantages of the proposed method compared with the existing methods. © 2014, Springer-Verlag Berlin Heidelberg.
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Predicting and controlling the dynamics of infectious diseases

- Evans, Robin, Mammadov, Musa

  • Authors: Evans, Robin , Mammadov, Musa
  • Date: 2015
  • Subjects: Diseases , Hospitals , Mathematical model , Optimal control , Predictive models , Sociology , Statistics
  • Type: Text , Conference proceedings
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/100787 , vital:10598 , http://doi.org/10.1109/CDC.2015.7403061
  • Description: This paper introduces a new optimal control model to describe and control the dynamics of infectious diseases. In the present model, the average time to isolation (i.e. hospitalization) of infectious population is the main time-dependent parameter that defines the spread of infection. All the preventive measures aim to decrease the average time to isolation under given constraints. The model suggested allows one to generate a small number of possible future scenarios and to determine corresponding trajectories of infected population in different regions. Then, this information is used to find an optimal distribution of bed capabilities across countries/regions according to each scenario. © 2015 IEEE.
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A hybrid wrapper-filter approach to detect the source(s) of out-of-control signals in multivariate manufacturing process

- Huda, Shamsul, Abdollahian, Mali, Mammadov, Musa, Yearwood, John, Ahmed, Shafiq, Sultan, Ibrahim

  • Authors: Huda, Shamsul , Abdollahian, Mali , Mammadov, Musa , Yearwood, John , Ahmed, Shafiq , Sultan, Ibrahim
  • Date: 2014
  • Subjects: MD Multidisciplinary , Fault diagnosis , Global optimization , Multivariate control chart , Wrapper and filter approaches
  • Type: Text , Journal article
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160527 , vital:12220 , https://doi.org/10.1016/j.ejor.2014.02.032 , ISBN:0377-2217
  • Description: With modern data-Acquisition equipment and on-line computers used during production, it is now common to monitor several correlated quality characteristics simultaneously in multivariate processes. Multivariate control charts (MCC) are important tools for monitoring multivariate processes. One difficulty encountered with multivariate control charts is the identification of the variable or group of variables that cause an out-of-control signal. Expert knowledge either in combination with wrapper-based supervised classifier or a pre-filter with wrapper are the standard approaches to detect the sources of out-of-control signal. However gathering expert knowledge in source identification is costly and may introduce human error. Individual univariate control charts (UCC) and decomposition of T2 statistics are also used in many cases simultaneously to identify the sources, but these either ignore the correlations between the sources or may take more time with the increase of dimensions. The aim of this paper is to develop a source identification approach that does not need any expert-knowledge and can detect out-of-control signal in less computational complexity. We propose, a hybrid wrapper-filter based source identification approach that hybridizes a Mutual Information (MI) based Maximum Relevance (MR) filter ranking heuristic with an Artificial Neural Network (ANN) based wrapper. The Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA) has been combined with MR (MR-ANNIGMA) to utilize the knowledge about the intrinsic pattern of the quality characteristics computed by the filter for directing the wrapper search process. To compute optimal ANNIGMA score, we also propose a Global MR-ANNIGMA using non-functional relationship between variables which is independent of the derivative of the objective function and has a potential to overcome the local optimization problem of ANN training. The novelty of the proposed approaches is that they combine the advantages of both filter and wrapper approaches and do not require any expert knowledge about the sources of the out-of-control signals. Heuristic score based subset generation process also reduces the search space into polynomial growth which in turns reduces computational time. The proposed approaches were tested by exhaustive experiments using both simulated and real manufacturing data and compared to existing methods including independent filter, wrapper and Multivariate EWMA (MEWMA) methods. The results indicate that the proposed approaches can identify the sources of out-of-control signals more accurately than existing approaches. © 2014 Elsevier B.V. All rights reserved.
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A new loss function for robust classification

- Zhao, Lei, Mammadov, Musa, Yearwood, John

  • Authors: Zhao, Lei , Mammadov, Musa , Yearwood, John
  • Date: 2014
  • Subjects: 0801 Artificial Intelligence and Image Processing , 0804 Data Format , 1702 Cognitive Science , Classification , Data mining , Loss function , Machine learning , Optimization
  • Type: Text , Journal article
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160646 , vital:12249 , https://doi.org/10.3233/IDA-140664 , ISBN:1088-467X
  • 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.
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Sigma supporting cone and optimality conditions in non-convex problems

- Hassani, Sara, Mammadov, Musa

  • Authors: Hassani, Sara , Mammadov, Musa
  • Date: 2014
  • Subjects: 0101 Pure Mathematics , 0102 Applied Mathematics , Non-convex analysis , Nonconvex sets , Optimality conditions , Supporting cone
  • Type: Text , Journal article
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/76323 , vital:7533 , http://www.scopus.com/inward/record.url?eid=2-s2.0-84911435293&partnerID=40&md5=0ca6dfd2787a35849a828b807633d41a , ISSN:0972-0871
  • Description: In this paper, a new supporting function for characterizing non-convex sets is introduced. The notions of σ-supporting cone and maximal conic gap are proposed and some properties are investigated. By applying these new notions, we establish the optimality conditions considered in [7] for a broader class of finite dimensional normed spaces in terms of weak subdifferentials.
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Structure learning of Bayesian Networks using global optimization with applications in data classification

- Taheri, Sona, Mammadov, Musa

  • Authors: Taheri, Sona , Mammadov, Musa
  • Date: 2014
  • Subjects: 0102 Applied Mathematics , 0103 Numerical and Computational Mathematics , Bayesian Networks , Data classification , Algorithms , Artificial intelligence , Classification (of information) , Directed graphs , Knowledge based systems , Learning systems , Shape optimization , Structural optimization , Uncertainty analysis , Directed acyclic graph (DAG) , Global optimization method , Model uncertainties , Optimization approach , Optimization modeling , Optimization procedures , UCI machine learning repository
  • Type: Text , Journal article
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/89644 , vital:9301 , http://doi.org/10.1007/s11590-014-0803-1 , ISSN:1862-4472
  • Description: Bayesian Networks are increasingly popular methods of modeling uncertainty in artificial intelligence and machine learning. A Bayesian Network consists of a directed acyclic graph in which each node represents a variable and each arc represents probabilistic dependency between two variables. Constructing a Bayesian Network from data is a learning process that consists of two steps: learning structure and learning parameter. Learning a network structure from data is the most difficult task in this process. This paper presents a new algorithm for constructing an optimal structure for Bayesian Networks based on optimization. The algorithm has two major parts. First, we define an optimization model to find the better network graphs. Then, we apply an optimization approach for removing possible cycles from the directed graphs obtained in the first part which is the first of its kind in the literature. The main advantage of the proposed method is that the maximal number of parents for variables is not fixed a priory and it is defined during the optimization procedure. It also considers all networks including cyclic ones and then choose a best structure by applying a global optimization method. To show the efficiency of the algorithm, several closely related algorithms including unrestricted dependency Bayesian Network algorithm, as well as, benchmarks algorithms SVM and C4.5 are employed for comparison. We apply these algorithms on data classification; data sets are taken from the UCI machine learning repository and the LIBSVM. © 2014, Springer-Verlag Berlin Heidelberg.
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Turnpike theorem for an infinite horizon optimal control problem with time delay

- Mammadov, Musa

A new auxiliary function method for general constrained global optimization

- Wu, Zhiyou, Bai, Fusheng, Yang, Yongjian, Mammadov, Musa

  • Authors: Wu, Zhiyou , Bai, Fusheng , Yang, Yongjian , Mammadov, Musa
  • Date: 2013
  • Subjects: 0102 Applied Mathematics , 0103 Numerical and Computational Mathematics , Auxiliary function method , General constrained global optimization
  • Type: Text , Journal article
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/42487 , vital:5097 , http://dx.doi.org/10.1080/02331934.2011.571258 , ISSN:0233-1934
  • Description: In this article, we first propose a method to obtain an approximate feasible point for general constrained global optimization problems (with both inequality and equality constraints). Then we propose an auxiliary function method to obtain a global minimizer or an approximate global minimizer with a required precision for general global optimization problems by locally solving some unconstrained programming problems. Some numerical examples are reported to demonstrate the efficiency of the present optimization method. © 2013 Taylor & Francis. , 2003011103
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Attribute weighted Naive Bayes classifier using a local optimization

- Taheri, Sona, Yearwood, John, Mammadov, Musa, Seifollahi, Sattar

  • Authors: Taheri, Sona , Yearwood, John , Mammadov, Musa , Seifollahi, Sattar
  • Date: 2013
  • Subjects: 1702 Cognitive Science , 0801 Artificial Intelligence and Image Processing , Classification , Naive bayes , Attribute weighting , Local optimization
  • Type: Text , Journal article
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/59808 , vital:5114 , http://dx.doi.org/10.1007/s00521-012-1329-z , ISSN:0941-0643
  • 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.
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Capped K-NN Editing in definition lacking environments

- Stranieri, Andrew, Yatsko, Andrew, Golden, Isaac, Mammadov, Musa, Bagirov, Adil

  • Authors: Stranieri, Andrew , Yatsko, Andrew , Golden, Isaac , Mammadov, Musa , Bagirov, Adil
  • Date: 2013
  • Subjects: Class noise , Data editing , Instance selection , Fuzzy classification
  • Type: Text , Journal article
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/71255 , vital:6730 , ISSN:1558-884X
  • Description: While any input may be contributing, imprecise specification of class of data subdivided into classes identifies as rather common a source of noise. The misrepresentation may be characteristic of the data or be caused by forcing of a regression problem into the classification type. Consideration is given to examples of this nature, and an alternative is proposed. In the main part, the approach is based on a well-known technique of data treatment for noise using k-NN. The paper advances an editing technique designed around idea of variable number of authenticating instances. Test runs performed on publicly available and proprietary data demonstrate high retention ability of the new procedure without loss of classification accuracy. Noise reduction methods in a broader classification context are extensively surveyed.
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Globally convergent algorithms for solving unconstrained optimization problems

- Taheri, Sona, Mammadov, Musa, Seifollahi, Sattar

  • Authors: Taheri, Sona , Mammadov, Musa , Seifollahi, Sattar
  • Date: 2013
  • Subjects: 0102 Applied Mathematics , 0103 Numerical and Computational Mathematics , Unconstrained optimization , Gradient method , Newton's method , Quasi-Newton method , Global convergence , Superlinear convergence
  • Type: Text , Journal article
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/60698 , vital:5115 , http://dx.doi.org/10.1080/02331934.2012.745529 , ISSN:0233-1934
  • Description: New algorithms for solving unconstrained optimization problems are presented based on the idea of combining two types of descent directions: the direction of anti-gradient and either the Newton or quasi-Newton directions. The use of latter directions allows one to improve the convergence rate. Global and superlinear convergence properties of these algorithms are established. Numerical experiments using some unconstrained test problems are reported. Also, the proposed algorithms are compared with some existing similar methods using results of experiments. This comparison demonstrates the efficiency of the proposed combined methods.
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Learning the naive bayes classifier with optimization models

- Taheri, Sona, Mammadov, Musa

  • Authors: Taheri, Sona , Mammadov, Musa
  • Date: 2013
  • Subjects: 01 Mathematical Sciences , 08 Information and Computing Sciences , Bayesian networks , Discretization , Naive Bayes classifier , Optimization
  • Type: Text , Journal article
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/37128 , vital:5759 , ISSN:1641-876X
  • Description: Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three novel optimization models for the naive Bayes classifier where both class probabilities and conditional probabilities are considered as variables. The values of these variables are found by solving the corresponding optimization problems. Numerical experiments are conducted on several real world binary classification data sets, where continuous features are discretized by applying three different methods. The performances of these models are compared with the naive Bayes classifier, tree augmented naive Bayes, the SVM, C4.5 and the nearest neighbor classifier. The obtained results demonstrate that the proposed models can significantly improve the performance of the naive Bayes classifier, yet at the same time maintain its simple structure.
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Preface: Special issue of JOGO MEC EurOPT 2010-Izmir

- Kasimbeyli, Refail, Mammadov, Musa, Dincer, Cemali

A new method for solving linear ill-posed problems

- Zhang, Jianjun, Mammadov, Musa

  • Authors: Zhang, Jianjun , Mammadov, Musa
  • Date: 2012
  • Subjects: 0103 Numerical and Computational Mathematics , 0102 Applied Mathematics , Generalized cross validation , Ill-posed problems , L-curve , Newton's method , Tikhonov regularization
  • Type: Text , Journal article
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/57681 , vital:4667 , http://dx.doi.org/10.1016/j.amc.2012.03.098 , ISSN:0096-3003
  • Description: In this paper, we propose a new method for solving large-scale ill-posed problems. This method is based on the Karush-Kuhn-Tucker conditions, Fisher-Burmeister function and the discrepancy principle. The main difference from the majority of existing methods for solving ill-posed problems is that, we do not need to choose a regularization parameter in advance. Experimental results show that the proposed method is effective and promising for many practical problems. © 2012.
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Application of optimisation-based data mining techniques to medical data sets: A comparative analysis

- Dzalilov, Zari, Bagirov, Adil, Mammadov, Musa

  • Authors: Dzalilov, Zari , Bagirov, Adil , Mammadov, Musa
  • Date: 2012
  • Subjects: Data mining , Optimisation , Cystic fibrosis , Tobacco control
  • Type: Text , Conference paper
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/73373 , vital:7042
  • Description: Abstract - Computational methods have become an important tool in the analysis of medical data sets. In this paper, we apply three optimisation-based data mining methods to the following data sets: (i) a cystic fibrosis data set and (ii) a tobacco control data set. Three algorithms used in the analysis of these data sets include: the modified linear least square fit, an optimization based heuristic algorithm for feature selection and an optimization based clustering algorithm. All these methods explore the relationship between features and classes, with the aim of determining contribution of specific features to the class outcome. However, the three algorithms are based on completely different approaches. We apply these methods to solve feature selection and classification problems. We also present comparative analysis of the algorithms using computational results. Results obtained confirm that these algorithms may be effectively applied to the analysis of other (bio)medical data sets
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Global stabilization in nonlinear discrete systems with time-delay

- Ivanov, Anatoli, Mammadov, Musa, Trofimchuk, Sergei

  • Authors: Ivanov, Anatoli , Mammadov, Musa , Trofimchuk, Sergei
  • Date: 2012
  • Subjects: Global asymptotic stability , Optimal control , Scalar difference equations with delay , Turnpike property in time-delay systems
  • Type: Text , Journal article
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/37224 , vital:4549 , http://dx.doi.org/10.1007/s10898-012-9862-y , ISSN:0925-5001
  • Description: A class of scalar nonlinear difference equations with delay is considered. Sufficient conditions for the global asymptotic stability of a unique equilibrium are given. Applications in economics and other fields lead to consideration of associated optimal control problems. An optimal control problem of maximizing a consumption functional is stated. The existence of optimal solutions is established and their stability (the turnpike property) is proved. © 2012 Springer Science+Business Media, LLC.
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Profiling phishing activity based on hyperlinks extracted from phishing emails

- Yearwood, John, Mammadov, Musa, Webb, Dean

  • Authors: Yearwood, John , Mammadov, Musa , Webb, Dean
  • Date: 2012
  • Subjects: Phishing , Profiling phishing emails , Multi-label classification , 0199 Other Mathematical Sciences , 0806 Information Systems , 0899 Other Information and Computing Sciences
  • Type: Text , Journal article
  • Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/163038 , vital:12761 , https://dx.doi.org/10.1007/s13278-011-0031-y , ISBN:1869-5450
  • Description: Phishing activity has recently been focused on social networking sites as a more effective way of exploiting not only the technology but also the trust that may exist between members in a social network. In this paper, a novel method for profiling phishing activity from an analysis of phishing emails is proposed. Profiling is useful in determining the activity of an individual or a particular group of phishers. Work in the area of phishing is usually aimed at detection of phishing emails. In this paper, we concentrate on profiling as distinct from detection of phishing emails. We formulate the profiling problem as a multi-label classification problem using the hyperlinks in the phishing emails as features and structural properties of emails along with whois (i.e. DNS) information on hyperlinks as profile classes. Further, we generate profiles based on the classifier predictions. Thus, classes become elements of profiles. We employ a boosting algorithm (AdaBoost) as well as SVM to generate multi-label class predictions on three different datasets created from hyperlink information in phishing emails. These predictions are further utilized to generate complete profiles of these emails. Results show that profiling can be done with quite high accuracy using hyperlink information.
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