Vitor Basto Fernandes

Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms

The 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 classification 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 research and propose two major advancements: Firstly we formulate the algorithm in detection error tradeoff space, minimizing false positives and false negatives, with the advantage that misclassification cost tradeoff can be assessed directly. Secondly, we add complexity as an objective function, which gives rise to a 3D objective space (as opposed to a 2D previous ROC space). A domain specific performance indicator for 3D Pareto front approximations, 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 using 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.

Information Architecture For IS Function: A Case Study

Todays complex, unstable and competitive society raises several difficulties to organisations. In this context, Information and Communications Technologies (ICT) and information itself have become resources of vital importance. The pressing need for Information Systems (IS) to meet several business requirements, in addition to the complexity involved in technology and business management, turns the IS Function one of the main areas of influence for success of modern organisations. Through its capacity of representing activities, management objects and corresponding relations, the Information Architecture of the Information Systems Function (IAISF), a technique derived from the well-known Information Architecture but exclusively focused on the Information Systems Function (ISF), allows not only the conceptualization and understanding of the ISF itself, but also of its interactions with other areas within organizations. This paper presents the main results of a case study related to the application of the IAISF technique in a computer service centre of a University.

3D fast convex-hull-based evolutionary multiobjective optimization algorithm

Abstract The receiver operating characteristic (ROC) and detection error tradeoff (DET) curves have been widely used in the machine learning community to analyze the performance of classifiers. The area (or volume) under the convex hull has been used as a scalar indicator for the performance of a set of classifiers in ROC and DET space. Recently, 3D convex-hull-based evolutionary multiobjective optimization algorithm (3DCH-EMOA) has been proposed to maximize the volume of convex hull for binary classification combined with parsimony and three-way classification problems. However, 3DCH-EMOA revealed high consumption of computational resources due to redundant convex hull calculations and a frequent execution of nondominated sorting. In this paper, we introduce incremental convex hull calculation and a fast replacement for non-dominated sorting. While achieving the same high quality results, the computational effort of 3DCH-EMOA can be reduced by orders of magnitude. The average time complexity of 3DCH-EMOA in each generation is reduced from O ( n 2 log n ) to O ( n log n ) per iteration, where n is the population size. Six test function problems are used to test the performance of the newly proposed method, and the algorithms are compared to several state-of-the-art algorithms, including NSGA-III, RVEA, etc., which were not compared to 3DCH-EMOA before. Experimental results show that the new version of the algorithm (3DFCH-EMOA) can speed up 3DCH-EMOA for about 30 times for a typical population size of 300 without reducing the performance of the method. Besides, the proposed algorithm is applied for neural networks pruning, and several UCI datasets are used to test the performance.

RuleSIM: a toolkit for simulating the operation and improving throughput of rule-based spam filters

This paper introduces RuleSIM, a toolkit comprising different simulation tools specifically designed to aid researchers concerned about spam‐filtering throughput. RuleSIM allows easily designing, developing, simulating and comparing new scheduling heuristics using different filters and sets of e‐mails. Simulation results can be both graphically analysed, by using different complementary views, and quantitatively compared through several measures. Moreover, the underlying RuleSIM API can be easily integrated with third‐party Java optimization platforms to facilitate debugging and achieve better configurations for rule scheduling. RuleSIM is free software distributed under the terms of GNU Lesser General Public License, and both source code and documentation are publicly available at https://github.com/rulesim/v2.0. Copyright

Multiobjective sparse ensemble learning by means of evolutionary algorithms

Abstract Ensemble learning can improve the performance of individual classifiers by combining their decisions. The sparseness of ensemble learning has attracted much attention in recent years. In this paper, a novel multiobjective sparse ensemble learning (MOSEL) model is proposed. Firstly, to describe the ensemble classifiers more precisely the detection error trade-off (DET) curve is taken into consideration. The sparsity ratio (sr) is treated as the third objective to be minimized, in addition to false positive rate (fpr) and false negative rate (fnr) minimization. The MOSEL turns out to be augmented DET (ADET) convex hull maximization problem. Secondly, several evolutionary multiobjective algorithms are exploited to find sparse ensemble classifiers with strong performance. The relationship between the sparsity and the performance of ensemble classifiers on the ADET space is explained. Thirdly, an adaptive MOSEL classifiers selection method is designed to select the most suitable ensemble classifiers for a given dataset. The proposed MOSEL method is applied to well-known MNIST datasets and a real-world remote sensing image change detection problem, and several datasets are used to test the performance of the method on this problem. Experimental results based on both MNIST datasets and remote sensing image change detection show that MOSEL performs significantly better than conventional ensemble learning methods.

Corrigendum to ‘Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms’ [Information Sciences volumes 367–368 (2016) 80–104]

a Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi’an Shaanxi Province 710071, China b School of Technology and Management, Computer Science and Communications Research Centre, Polytechnic Institute of Leiria, 2411-901 Leiria, Portugal c Faculty of Technology, De Montfort University, Gateway House 5.33, The Gateway, LE1 9BH Leicester, UK d Multicriteria Optimization, Design, and Analytics Group, LIACS, Leiden University, Niels Bohrweg 1, 2333-CA Leiden, The Netherlands e Microsoft Research Asia, Beijing 100190, China f Nature Inspired Computation and Applications Laboratory (NICAL), the USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China (USTC), Hefei, Anhui 230027, China

Corrigendum to “A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification” [Applied Soft Computing Volume 48 (2016) 111–123]

School of Technology and Management, Computer Science and Communications Research Centre, Polytechnic Institute of Leiria, 2411-901 Leiria, Portugal School of Computer Science and Informatics, Faculty of Technology, De Montfort University, LE1 9BH Leicester, United Kingdom Informatics Engineering School, University of Vigo, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, International Research Center for Intelligent Perception nd Computation, Xidian University, Xian, Shaanxi Province 710071, China Leiden Institute of Advanced Computer Science, Faculty of Science, Leiden University, 2333-CA Leiden, The Netherlands