An evolutionary framework using particle swarm optimization for classification method PROAFTN

Abstract

The aim of this paper is to introduce a methodology based on the particle swarm optimization (PSO) algorithm to train the Multi-Criteria Decision Aid (MCDA) method PROAFTN. PSO is an efficient evolutionary optimization algorithm using the social behavior of living organisms to explore the search space. It is a relatively new population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. Furthermore, it is easy to code and robust to control parameters. To apply PROAFTN, the values of several parameters need to be determined prior to classification, such as boundaries of intervals and weights. In this study, the proposed technique is named PSOPRO, which utilizes PSO to elicit the PROAFTN parameters from examples during the learning process. To test the effectiveness of the methodology and the quality of the obtained models, PSOPRO is evaluated on 12 public-domain datasets and compared with the previous work applied on PROAFTN. The computational results demonstrate that PSOPRO is very competitive with respect to the most common classification algorithms.

Detecting Cyber-Attacks on Wireless Mobile Networks Using Multicriterion Fuzzy Classifier with Genetic Attribute Selection

“…The overall block diagram for the cyber-attack detection system is shown in Figure 2. It starts with the database of (2) ℎ: class index (3) : attribute's index (4) Select threshold for interval selection (5) Generate intervals using a discretization technique (6) Apply greedy hill climbing approach to select most relevant subsets (7) for each class do (8) for each attribute do (9) for every value in attribute do (10) Recursively check all values in the next attribute (11) if Frequency of values ⩾ then (12) Choose intervals for prototype ℎ (13) else (14) Discard interval and go next (i.e., 2 2 ℎ ) (15) end if (16) end for ( captured traffic. After preprocessing and analyzing traffic records and log files, it performs feature extraction to represent each instance with a vector of relevant attributes.…”

“…However, there is not much work done in the area of network security. In this paper, we investigate a new methodology for detecting cyber-attacks in wireless mobile networks based on multicriterion decision making fuzzy classification [15,16]. The proposed approach is combined with an attribute selection strategy based on genetic algorithms [17].…”

Combining Machine Learning and Metaheuristics Algorithms for Classification Method PROAFTN

“…The general description of the PSO methodology and its application is described in [6]. As discussed earlier, to apply PROAFTN, the pessimistic interval [S 1 jh , S 2 jh ] and the optimistic interval [q 1 jh , q 2 jh ] for each attribute in each class need to be determined, where:…”

A Fully Adaptive and Hybrid Method for Image Segmentation Using Multilevel Thresholding

“…The main advantages of PSO are its flexibility, its robustness and its inherent parallelism. It has been applied in many kinds of engineering problem widely and has proved its large capability to competing with other classical optimization algorithms [40,41,42,43,44]. However, observations reveal that the PSO suffers from a premature convergence: it converges rapidly in the early stages of the searching process, but can be trapped in a local optimum in the later stages.…”