Browsing by Author "Kareem, Shahab Wahhab"

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Bayes ağ yapılarının öğrenilmesi için yeni sürü zekası algoritmaları ve karşılaştırılmalı bir değerlendirme
(2020) Kareem, Shahab Wahhab; Okur, Mehmet Cudi
Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning which can represent probabilistic dependency relationships among the variables. A Bayesian network depends on; 1.the parameters of the network and 2.the structure. Parameters represent conditional probabilities while the structure represents dependencies between the random variables. The structure of a Bayesian network is a directed acyclic graph (DAG). Learning the structure of a Bayesian network is NP-hard but still extensive work have been done to optimize approximate solutions. In this thesis, we have conducted research for structure learning to develop algorithms to find a solution to the problem. There are two approaches for learning the structure of Bayesian networks. The first is a constraint-based approach, and the second is a score and a search approach. One common type of method for Bayesian network structure learning is the score-based search. Score-based methods rely on a function to test how well the network model matches the data, and they search for a structure that produces high scores on this function. There are two types of scoring functions: Bayesian score and information-theoretic score. The Bayesian and information-theoretic scores have been implemented in several structure learning methods. In this thesis, we focused on the score based search for testing the structure learning of Bayesian network using heuristic methods for searching and BDeu as a score function. In this thesis we proposed five algorithms for the search part and used BDeu as a score function. We also proposed a sixth method which is also a nature inspired one. The first proposed algorithm used Pigeon Inspired Optimization as a search method and the above mentioned score function. The proposed method has shown a good result when compared with default methods like Simulated Annealing iii and greedy search. This algorithm is a novel approach applied for structure learning of Bayesian network. The second proposed algorithm used Bee optimization and Simulated Annealing as a hybrid algorithm, which used Bee optimization as a local search and Simulated Annealing as a global search. The third proposed algorithm also used bee optimization and Simulated Annealing as a hybrid but used Bee optimization as a global search and Simulated Annealing as a local search. The fourth proposed algorithm used Bee optimization and Greedy search as a hybrid algorithm. It used Bee optimization as local search and Greedy as global search. The fifth algorithms also used bee optimization and Greedy as a hybrid algorithm, but it used Bee optimization as a global search and Greedy as a local search Our last proposed algorithm used Elephant Swarm Water Search Algorithm (ESWSA). The thesis presents the results of extensive evaluations of these algorithms based on common benchmark data sets. Applications of ESWSA in Structure learning of Bayesian Network and comparisons with the Simulated Annealing and Greedy Search, show that this proposed method is better than the default Simulated Annealing and Greedy search methods. Keywords: Bayesian network, structure learning, Pigeon Inspired Optimization, Bee Optimization, greedy, Simulated Annealing, elephant swarm search, water search, global search, local search, search and score.
Citation - Scopus: 8
Bayesian network structure learning based on pigeon inspired optimization
(World Academy of Research in Science and Engineering, 2019) Shahab Wahhab Kareem; Mehmet Cudi Okur; Kareem, Shahab Wahhab; Okur, Mehmet Cudi
Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning. Probabilistic dependency relationships among the variables can be represented by Bayesian networks. One strategy of a structure learning Bayesian Networks is the score and search technique. In this paper present the proposed method for Bayesian network structure learning which is depended on Pigeon Inspired Optimization (PIO). The proposed method is a simple one among a firm concentration rate. In nature a navigational ability concerning pigeons is unbelievable and impressive. Under the PIO search algorithm we define a set of directed acyclic graphs. Every graph owns a score which shows its fitness. It iterates the algorithm until it gets the best solution or a satisfactory network structure using a landmark compass and map operator. During this work the proposed method compared with Simulated Annealing and Greedy Search using BDe score function. We also investigated the confusion matrix performances of the methods using various benchmark data sets. Specific effects show that a presented algorithm produces excellent performance than Simulated Annealing and Greedy algorithms and produces higher scores and accuracy values. © 2019 Elsevier B.V. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 10
FALCON OPTIMIZATION ALGORITHM FOR BAYESIAN NETWORK STRUCTURE LEARNING
(AGH University of Science and Technology Press, 2021) Shahab Wahhab Kareem; Mehmet Cudi Okur; Kareem, Shahab Wahhab; Okur, Mehmet Cudi
In machine-learning some of the helpful scientific models during the production of a structure of knowledge are Bayesian networks. They can draw the relationships of probabilistic dependency among many variables. The score and search method is a tool that is used as a strategy for learning the structure of a Bayesian network. The authors apply the falcon optimization algorithm (FOA) to the learning structure of a Bayesian network. This paper has employed reversing deleting moving and inserting to obtain the FOA for approaching the optimal solution of a structure. Essentially the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is associated with pigeon-inspired optimization greedy search and simulated annealing that apply the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques by utilizing several benchmark data sets. As shown by the experimental evaluations the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values). © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 4
Citation - Scopus: 15
Pigeon Inspired Optimization of Bayesian Network Structure Learning and a Comparative Evaluation
(SEOUL NATL UNIV INST COGNITIVE SCIENCE, 2019) Shahab Wahhab Kareem; Mehmet Cudi Okur; Kareem, Shahab Wahhab; Okur, Mehmet Cudi
Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning. Probabilistic dependency relationships among the variables can be represented by Bayesian networks. One strategy of a structure learning Bayesian Networks is the score and search technique. In this paper we present a new method for structure learning of the Bayesian network which is based on Pigeon Inspired Optimization (PIO) Algorithm. The proposed algorithm is a simple one with fast convergence rate. In nature the navigational ability of pigeons is unbelievable and highly impressive. In accordance with the PIO search algorithm a set of directed acyclic graphs is defined. Every graph owns a score which shows its fitness. The algorithm is iterated until it gets the best solution or a satisfactory network structure using map and compass and landmark operator. In this work the proposed method compared with Simulated Annealing Bee optimization and Simulated Annealing as a hybrid algorithm. Bee optimization and Greedy search as a hybrid algorithm and Greedy Search using BDeu score function We also investigated the confusion matrix performances of the methods. The paper presents the results of extensive evaluations of these algorithms based on common benchmark data sets. The results indicate that the proposed algorithm has better performance than the other algorithms and produces higher scores and accuracy values.
Citation - WoS: 9
Citation - Scopus: 16
Structure learning of Bayesian networks using elephant swarm water search algorithm
(IGI Global, 2020) Shahab Wahhab Kareem; Mehmet Cudi Okur; Kareem, Shahab Wahhab; Okur, Mehmet Cudi
Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning. Bayesian networks can represent probabilistic dependency relationships among the variables. One strategy of Bayesian Networks structure learning is the score and search technique. The authors present the Elephant Swarm Water Search Algorithm (ESWSA) as a novel approach to Bayesian network structure learning. In the algorithm, Deleting Reversing Inserting and Moving are used to make the ESWSA for reaching the optimal structure solution. Mainly water search strategy of elephants during drought periods is used in the ESWSA algorithm. The proposed method is compared with simulated annealing and greedy search using BDe score function. The authors have also investigated the confusion matrix performances of these techniques utilizing various benchmark data sets. As presented by the results of the evaluations the proposed algorithm has better performance than the other algorithms and produces better scores and accuracy values. © 2021 Elsevier B.V. All rights reserved.