Browsing by Author "Maulana, A."
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Item Metadata only Maximizing Consensus in Portfolio Selection in Multicriteria Group Decision Making(Elsevier, 2016-10-04) Emmerich, M. T. M.; Deutz, A.; Li, Longmei; Maulana, A.; Yevseyeva, IrynaThis paper deals with a scenario of decision making where a moderator selects a (sub)set (aka portfolio) of decision alternatives from a larger set. The larger the number of decision makers who agree on a solution in the portfolio the more successful the moderator is. We assume that decision makers decide independently from each other but indicate their preferences with respect to different objectives in terms of desirability functions, which can be interpreted as cumulative (probability) density functions. A procedure to select a solution with maximal expected number of decision makers that accept it is provided. Moreover, this is generalized to sets of solutions. An algorithm for computing and maximizing the expected number of decision makers that can agree on at least one solution in a subset of decision alternatives is developed. Computational aspects, as well as practical examples for using this for item selection from a database will be discussed.Item Open Access Modularities maximization in multiplex network analysis using many-objective optimization(IEEE, 2017-02-13) Maulana, A.; Gametto, V.; Garlaschelli, D.; Yevseyeva, Iryna; Emmerich, M. T. M.Abstract: Nowadays, social network analysis receives big attention from academia, industries and governments. Some practical applications such as community detection and centrality in economic networks have become main issues in this research area. Community detection algorithm for complex network analysis is mainly accomplished by the Louvain Method that seeks to find communities by heuristically finding a partitioning with maximal modularity. Traditionally, community detection applied for a network that has homogeneous semantics, for instance indicating friend relationship between people or import-export relationships between countries etc. However we increasingly deal with more complex network and also with so-called multiplex networks. In a multiplex network the set of nodes stays the same, while there are multiple sets of edges. In the analysis we would like to identify communities, but different edge sets give rise to different modularity optimizing partitions into communities. We propose to view community detection of such multilayer networks as a many-objective optimization problem. For this apply Evolutionary Many Objective Optimization and compute the Pareto fronts between different modularity layers. Then we group the objective functions into community in order to better understand the relationship and dependence between different layers (conflict, indifference, complementarily). As a case study, we compute the Pareto fronts for model problems and for economic data sets in order to show how to find the network modularity tradeoffs between different layers.Item Open Access Multiobjective optimization of classifiers by means of 3-D convex hull based evolutionary algorithms(Elsevier, 2016-05-27) Zhao, J.; Basto-Fernandes, V.; Jiao, L.; Yevseyeva, Iryna; Maulana, A.; Li, R.; Back, T.; Emmerich, M. T. M.; Tang, KeThe receiver operating characteristic (ROC) and detection error tradeoff(DET) curves are frequently used in the machine learning community to analyze the performance of binary classifiers. Recently, the convex-hull-based multiobjective genetic programming algorithm was proposed and successfully applied to maximize the convex hull area for binary classifi- cation problems by minimizing false positive rate and maximizing true positive rate at the same time using indicator-based evolutionary algorithms. The area under the ROC curve was used for the performance assessment and to guide the search. Here we extend this re- search and propose two major advancements: Firstly we formulate the algorithm in detec- tion error tradeoffspace, minimizing false positives and false negatives, with the advantage that misclassification cost tradeoffcan be assessed directly. Secondly, we add complexity as an objective function, which gives rise to a 3D objective space (as opposed to a 2D pre- vious ROC space). A domain specific performance indicator for 3D Pareto front approxima- tions, the volume above DET surface, is introduced, and used to guide the indicator-based evolutionary algorithm to find optimal approximation sets. We assess the performance of the new algorithm on designed theoretical problems with different geometries of Pareto fronts and DET surfaces, and two application-oriented benchmarks: (1) Designing spam filters with low numbers of false rejects, false accepts, and low computational cost us- ing rule ensembles, and (2) finding sparse neural networks for binary classification of test data from the UCI machine learning benchmark. The results show a high performance of the new algorithm as compared to conventional methods for multicriteria optimization.