Enhanced quadratic approximation integrated with butterfly optimization: a new search algorithm tested on structural and mathematical problems

The Butterfly Optimization Algorithm (BOA) is a swarm based technique, inspired from mating and food searching process of butterflies, developed in last year. Experiments indicate that BOA provides substantial exploration capability on conventional non-constrained benchmark problems, however for the cases with more complex and noisy domains the algorithm can easily be trapped into local minima due to its restricted exploitation behavior. To tackle this issue, current study deals with introducing an alternative search strategy to explore the region of the search domain with high certainty. Such that, firstly a weighted agent is defined and then a quadratic search is performed in the vicinity of this pre-defined agent. This alternative search strategy is named as Enhanced Quadratic Approximation (EQA) and it is combined with BOA method to improve its exploitation behavior and provide an efficient search algorithm. Thus, obtained new method is named as Enhanced Quadratic Approximation Integrated with Butterfly Optimization (EQB) algorithm. Different properties of proposed EQB are tested on mathematical and structural benchmark problems. Acquired results show that the introduced algorithm, in comparison with its parent method and some other well-stablished reported algorithms in the literature, provides a competitive performance in terms of stability, accuracy and convergence rate.

A New Robust Classifier on Noise Domains: Bagging of Credal C4.5 Trees

The knowledge extraction from data with noise or outliers is a complex problem in the data mining area. Normally, it is not easy to eliminate those problematic instances. To obtain information from this type of data, robust classifiers are the best option to use. One of them is the application of bagging scheme on weak single classifiers. The Credal C4.5 (CC4.5) model is a new classification tree procedure based on the classical C4.5 algorithm and imprecise probabilities. It represents a type of the so-calledcredal trees. It has been proven that CC4.5 is more robust to noise than C4.5 method and even than other previous credal tree models. In this paper, the performance of the CC4.5 model in bagging schemes on noisy domains is shown. An experimental study on data sets with added noise is carried out in order to compare results where bagging schemes are applied on credal trees and C4.5 procedure. As a benchmark point, the known Random Forest (RF) classification method is also used. It will be shown that the bagging ensemble using pruned credal trees outperforms the successful bagging C4.5 and RF when data sets with medium-to-high noise level are classified.

PIECEWISE LINEAR REPRESENTATION SEGMENTATION IN NOISY DOMAINS WITH A LARGE NUMBER OF MEASUREMENTS: THE AIR TRAFFIC CONTROL DOMAIN

The importance of time series segmentation techniques is rapidly expanding, due to the growth in collection and storage technologies. Among them, one of the most used ones is Piecewise Linear Representation, probably due to its ease of use. This work tries to determine the difficulties faced by this technique when the analyzed time series shows noisy data and a large number of measurements and how to introduce the information about the present noise in the segmentation process. Both difficulties are met in the Air Traffic Control domain, which exhibits position measurements of aircraft's trajectories coming from sensor devices (basically surveillance radar and aircraft-derived data), being used as the motivating domain. Results from the three main traditional techniques are presented (sliding window, top down and bottom up approaches) and compared with a new introduced approach, the Hybrid Local Residue Analysis technique.