An ensemble technique for multi class imbalanced problem using probabilistic neural networks
- Authors: Chandrasekara, N. , Tilakaratne, Chandima , Mammadov, Musa
- Date: 2018
- Type: Text , Journal article
- Relation: Advances and Applications in Statistics Vol. 53, no. 6 (2018), p. 647-658
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- Description: The class imbalanced problem is one of the major difficulties encountered by many researchers when using classification tools. Multi class problems are especially severe in this regard. The main objective of this study is to propose a suitable technique to handle multi class imbalanced problem. Probabilistic neural network (PNN) is used as the classification tool and the directional prediction of Australian, United States and Srilankan stock market indices is considered as the application. We propose an ensemble technique to handle multi class imbalanced problem that is called multi class undersampling based bagging (MCUB) technique. This is a new initiative that has not been considered in the literature to handle multi class imbalanced problem by employing PNN. The results obtained demonstrate that the proposed MCUB technique is capable of handling multi class imbalanced problem. Therefore, the PNN with the proposed ensemble technique can be used effectively in data classification. As a further study, other classification tools can be used to investigate the performance of the proposed MCUB technique in solving class imbalanced problems.
Target learning : A novel framework to mine significant dependencies for unlabeled data
- Authors: Wang, Limin , Chen, Shenglei , Mammadov, Musa
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018; Melbourne, Australia; 3rd-6th June 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10937 LNAI, p. 106-117
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- 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.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A new reliability analysis method based on the conjugate gradient direction
- Authors: Ezzati, Ghasem , Mammadov, Musa , Kulkarni, Siddhivinayak
- Date: 2015
- Type: Text , Journal article
- Relation: Structural and Multidisciplinary Optimization Vol. 51, no. 1 (2015), p. 89-98
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- 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.
A hybrid wrapper-filter approach to detect the source(s) of out-of-control signals in multivariate manufacturing process
- Authors: Huda, Shamsul , Abdollahian, Mali , Mammadov, Musa , Yearwood, John , Ahmed, Shafiq , Sultan, Ibrahim
- Date: 2014
- Type: Text , Journal article
- Relation: European Journal of Operational Research Vol. 237, no. 3 (2014), p. 857-870
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- 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.
A new loss function for robust classification
- Authors: Zhao, Lei , Mammadov, Musa , Yearwood, John
- Date: 2014
- Type: Text , Journal article
- Relation: Intelligent Data Analysis Vol. 18, no. 4 (2014), p. 697-715
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- Description: Loss function plays an important role in data classification. Manyloss functions have been proposed and applied to differentclassification problems. This paper proposes a new so called thesmoothed 0-1 loss function, that could be considered as anapproximation of the classical 0-1 loss function. Due to thenon-convexity property of the proposed loss function, globaloptimization methods are required to solve the correspondingoptimization problems. Together with the proposed loss function, wecompare the performance of several existing loss functions in theclassification of noisy data sets. In this comparison, differentoptimization problems are considered in regards to the convexity andsmoothness of different loss functions. The experimental resultsshow that the proposed smoothed 0-1 loss function works better ondata sets with noisy labels, noisy features, and outliers. © 2014 - IOS Press and the authors. All rights reserved.
Sigma supporting cone and optimality conditions in non-convex problems
- Authors: Hassani, Sara , Mammadov, Musa
- Date: 2014
- Type: Text , Journal article
- Relation: Far East Journal of Mathematical Sciences Vol. 91, no. 2 (2014), p. 169-190
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- 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.
Capped K-NN Editing in definition lacking environments
- Authors: Stranieri, Andrew , Yatsko, Andrew , Golden, Isaac , Mammadov, Musa , Bagirov, Adil
- Date: 2013
- Type: Text , Journal article
- Relation: Journal of Pattern Recognition Research Vol. 8, no. 1 (2013), p. 39-58
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- 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.
Preface: Special issue of JOGO MEC EurOPT 2010-Izmir
- Authors: Kasimbeyli, Refail , Mammadov, Musa , Dincer, Cemali
- Date: 2013
- Type: Text , Journal article
- Relation: Journal of Global Optimization Vol. 56, no. 2 (June 2013), p. 217-218
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- Reviewed:
- Description: C1
Application of optimisation-based data mining techniques to medical data sets: A comparative analysis
- Authors: Dzalilov, Zari , Bagirov, Adil , Mammadov, Musa
- Date: 2012
- Type: Text , Conference paper
- Relation: IMMM 2102: The Second International Conference on Advances in Information Mining and Management p. 41-46
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- 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
Global stabilization in nonlinear discrete systems with time-delay
- Authors: Ivanov, Anatoli , Mammadov, Musa , Trofimchuk, Sergei
- Date: 2012
- Type: Text , Journal article
- Relation: Journal of Global Optimization Vol.56, no. 2 (2012), p. 1-13
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- 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.
Profiling phishing activity based on hyperlinks extracted from phishing emails
- Authors: Yearwood, John , Mammadov, Musa , Webb, Dean
- Date: 2012
- Type: Text , Journal article
- Relation: Social Network Analysis and Mining Vol. 2, no. 1 (2012), p. 5-16
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- 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.
Global Optimality Conditions and Optimization Methods for Quadratic Knapsack Problems
- Authors: Wu, Zhiyou , Yang, Y. J. , Bai, Fusheng , Mammadov, Musa
- Date: 2011
- Type: Text , Journal article
- Relation: Journal of Optimization Theory and Applications Vol. 151, no. 2 (2011), p. 241-259
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- Description: The quadratic knapsack problem (QKP) maximizes a quadratic objective function subject to a binary and linear capacity constraint. Due to its simple structure and challenging difficulty, it has been studied intensively during the last two decades. This paper first presents some global optimality conditions for (QKP), which include necessary conditions and sufficient conditions. Then a local optimization method for (QKP) is developed using the necessary global optimality condition. Finally a global optimization method for (QKP) is proposed based on the sufficient global optimality condition, the local optimization method and an auxiliary function. Several numerical examples are given to illustrate the efficiency of the presented optimization methods. © 2011 Springer Science+Business Media, LLC.
Optimization of a quarter-car suspension model coupled with the driver biomechanical effects
- Authors: Kuznetsov, Alexey , Mammadov, Musa , Sultan, Ibrahim , Hajilarov, Eldar
- Date: 2011
- Type: Text , Journal article
- Relation: Journal of Sound and Vibration Vol. 330, no. 12 (2011), p. 2937-2946
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- Description: In this paper a Human–Vehicle–Road (HVR) model, comprising a quarter-car and a biomechanical representation of the driver, is employed for the analysis. Differential equations are provided to describe the motions of various masses under the influence of a harmonic road excitation. These equations are, subsequently, solved to obtain a closed form mathematical expression for the steady-state vertical acceleration measurable at the vehicle–human interface. The solution makes it possible to find optimal parameters for the vehicle suspension system with respect to a specified ride comfort level. The quantitative definition given in the ISO 2631 standard for the ride comfort level is adopted in this paper for the optimization procedure. Numerical examples, based on actually measured road profiles, are presented to prove the validity of the proposed approach and its suitability for the problem at hand.
Optimization of improved suspension system with inerter device of the quarter-car model in vibration analysis
- Authors: Kuznetsov, Alexey , Mammadov, Musa , Sultan, Ibrahim , Hajilarov, Eldar
- Date: 2011
- Type: Text , Journal article
- Relation: Archive of Applied Mechanics Vol. 81, no. 10 (2011), p. 1427-1437
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- Description: In this paper, we analyze an improved suspension system with the incorporated inerter device of the quarter-car model to obtain optimal design parameters for maximum comfort level for a driver and passengers. That is achieved by finding the analytical solution for the system of ordinary differential equations, which enables us to generate an optimization problem whose objective function is based on the international standards of admissible acceleration levels (ISO 2631-1, Mechanical Vibration and Shock-Evaluation of Human Exposure to Whole-Body Vibration-Part 1, 1997). The considered approach ensures the highest level of comfort for the driver and passengers due to a favorable reduction in body vibrations. Numerical examples, based on actually measured road profiles, are presented at the end of the paper to prove the validity of the proposed approach and its suitability for the problem at hand. © 2010 Springer-Verlag.
Tree augmented naive bayes based on optimization
- Authors: Taheri, Sona , Mammadov, Musa
- Date: 2011
- Type: Text , Conference paper
- Relation: 42 Annual Iranian Mathematics Conference Vali-a-Asr University of Rasanjan 5th-8th September, 2011 p. 594-598
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- Description: Tree augmented naive Bayes is a semi-naive Bayesian Learning method. It relaxes the naive Bayes attribute independence assumption by employing a tree structure, in which each attribute only depends on the class and one other attribute. A maximum weighted spanning tree that maximizes the likelihood of the training data is used to perform classification.
- Description: 2003009354
A globally optimization algorithm for systems of nonlinear equations
- Authors: Mammadov, Musa , Taheri, Sona
- Date: 2010
- Type: Text , Conference paper
- Relation: Proceedings of PCO 2010, The Third International Conference on Power Control and Optimization 2010 Gold Coast p. 214-234
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- Description: In this paper, a new algorithm is proposed for the solutions of system of nonlinear equations. This algorithm uses a combination of the gradient and Newton's methods. A novel dynamic combinator is developed to determine the contribution of the methods in the combination. Also, by using some parameters in the proposed algorithm, this contribution is adjusted. The efficiency of the algoritms is studied in solving system of nonlinear equations.
A novel approach for predicting trading signals of a stock market index
- Authors: Tilakaratne, Chandima , Mammadov, Musa , Morris, Sidney
- Date: 2010
- Type: Text , Book chapter
- Relation: Forecasting models: Methods and applications p. 145-160
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Application of optimisation-based data mining techniques to tobacco control dataset
- Authors: Dzalilov, Zari , Zhang, J , Bagirov, Adil , Mammadov, Musa
- Date: 2010
- Type: Text , Journal article
- Relation: International Journal of Lean Thinking Vol. 1, no. 1 (2010), p. 27-41
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- Description: Tobacco smoking is one of the leading causes of death around the world. Consequently, control of tobacco use is an important global public health issue. Tobacco control may be aided by development of theoretical and methodological frameworks for describing and understanding complex tobacco control systems. Linear regression and logistic regression are currently very popular statistical techniques for modeling and analyzing complex data in tobacco control systems. However, in tobacco markets, numerous interrelated factors nontrivially interact with tobacco control policies, such that policies and control outcomes are nonlinearly related.
Global stability, periodic solutions and optimal control in a nonlinear differential delay model
- Authors: Ivanov, Anatoli , Mammadov, Musa
- Date: 2010
- Type: Text , Conference proceedings
- Relation: Eighth Mississippi State - UAB Conference on Differential Equations and Computational Simulations, 2010
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- Description: A nonlinear differential equation with delay serving as a mathematical model of several applied problmes is considered. Sufficient conditions for the global asymptotic stability and for the existence of periodic solutions are given. Two particular applications are treated in detail. The first one is a blood cell production model by Mackey, for which new periodicity criteria are derived. The second application is a modified economic model with delay due to Ramsay. An optimization problem for a maximal consumption is stated and solved for the latter.
Regularization methods in the study of drug reaction relationships
- Authors: Mammadov, Musa , Zhao, Lei , Zhang, Jianjun
- Date: 2010
- Type: Text , Conference proceedings
- Full Text: false