A self-adaptive multi-objective feature selection approach for classification problems

Xue, Yu; Zhu, Haokai; Neri, Ferrante

doi:10.3233/ica-210664

Cited by 21 publications

(10 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mathematical transcription of this multi-objective optimization problem is as follows [28,[78][79][80][81][82]: The solution 𝐵 𝑟 to this multi-objective optimization problem is generally not unique and not optimal because they represent a compromise on the two objectives to be achieved. Indeed, the excessive reduction of the number of features could decrease the accuracy.…”

Section: Mathematical Modeling Of the Wrapper Feature Selection In A ...mentioning

confidence: 99%

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

SASSI,

CHELOUAH

2024

Preprint

View full text Add to dashboard Cite

Faced with the increase in high-dimensional Big Data creating more volume and complexity, the feature selection process became an essential phase in the preprocessing workflow upstream of the design of systems based on deep learning. This paper is a concrete and first application of the new metaheuristic Harris Hawk Optimization Encirclement-Attack-Synergy (HHO-EAS) in solving the NP-Hard wrapper feature selection multi-objective optimization problem. This problem combines two contradictory objectives: maximizing the accuracy of a classifier while minimizing the number of the most relevant and non-redundant selected features. To do this we hybridized HHO-EAS to create the new metaheuristic Binary HHO-EAS (BHHO-EAS). We combined HHO-EAS to the sixteen transfer functions most used in the literature structured in a balanced way among the four main categories including S-Shaped, V-Shaped, Q-Shaped and U-Shaped. This wide range of transfer function allows us to analyze the evolution of BHHO-EAS’s skills according to the assigned transfer function and to determine which of them offer the best performances. We applied wrapper feature selection to the well-known NSL-KDD dataset with the deep learning Multi Layer Perceptron (MLP) classifier. We put BHHO-EAS in competition with three other well-known population based binary metaheuristics, BPSO, BBA and BHHO. The analysis of the experimental results, compared to the three other binary metaheuristics, demonstrated that BHHO-EAS obtained the best performance on 100% of the transfer functions. This is more particularly highlighted by the U-Shaped transfer functions, which give an acceptable compromise for the two objectives of the problem with an average accuracy of 96,4% and an average size of selected features of 20.

show abstract

Section: Mathematical Modeling Of the Wrapper Feature Selection In A ...mentioning

confidence: 99%

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

SASSI,

CHELOUAH

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…This will prevent the model from growing excessively while still maintaining accuracy. Concerning space and time complexity, we will use unsupervised learning techniques combined with dimensionality reduction and feature selection techniques [59] to determine which and how many types of fixture exist in a given network, according to their characteristics. Then, we will use their type instead of their unique identifier to identify them.…”

Section: Efficiency Pattern Classification On Optimal Dimming Rangementioning

confidence: 99%

Knowledge-based decision intelligence in street lighting management

Sousa

Teixeira

Carneiro

et al. 2022

ICA

View full text Add to dashboard Cite

As the availability of computational power and communication technologies increases, Humans and systems are able to tackle increasingly challenging decision problems. Taking decisions over incomplete visions of a situation is particularly challenging and calls for a set of intertwined skills that must be put into place under a clear rationale. This work addresses how to deliver autonomous decisions for the management of a public street lighting network, to optimize energy consumption without compromising light quality patterns. Our approach is grounded in an holistic methodology, combining semantic and Artificial Intelligence principles to define methods and artefacts for supporting decisions to be taken in the context of an incomplete domain. That is, a domain with absence of data and of explicit domain assertions.

show abstract

“…Optical data are brain structural magnetic resonance scanning images (sMRI) or brain functional magnetic resonance scanning images (fMRI). Using numerical or visual data to train an ML algorithm for ASD diagnosis is ordinarily possible by determining the distinguishing features or using an automated feature extraction technique [43][44][45]. These features may be structural gray matter (GM) values acquired from cortical thickness (CT) [46][47][48], GM density (GMd) values from voxel-based morphometry (VBM) [49], diffusion-weighted imaging (DWI) [fractional anisotropy (FA)] in white matter (WM)) microorganism changes [50], connectivity matrices [51], parameters from network analysis [52][53][54], and resting/duty state fMRI information [55,56].…”

Section: Introductionmentioning

confidence: 99%

Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning

Nogay,

Adeli

2023

Preprint

View full text Add to dashboard Cite

The fact that the rapid and definitive diagnosis of autism cannot be made today and that autism cannot be treated provides an impetus to look into novel technological solutions. To contribute to the resolution of this problem through multiple classifications by considering age and gender factors, in this study, two quadruple and one octal classifications were performed using a deep learning (DL) approach. Gender in one of the four classifications and age groups in the other were considered. In the octal classification, classes were created considering gender and age groups. In addition to the diagnosis of ASD (Autism Spectrum Disorders), another goal of this study is to find out the contribution of gender and age factors to the diagnosis of ASD by making multiple classifications based on age and gender for the first time. Brain structural MRI (sMRI) scans of participators with ASD and TD (Typical Development) were pre-processed in the system originally designed for this purpose. Using the Canny Edge Detection (CED) algorithm, the sMRI image data was cropped in the data pre-processing stage, and the data set was enlarged five times with the data augmentation (DA) techniques. The most optimal convolutional neural network (CNN) models were developed using the grid search optimization (GSO) algorism. The proposed DL prediction system was tested with the five-fold cross-validation technique. The accuracy rates acquired for all three CNN models designed to be utilized within the system were compared with successfull pre-trained CNN models through the transfer learning (TL) practice. As a result, it was revealed that age and gender factors were effective in the diagnosis of ASD with the system developed for ASD multiple classifications, and it was seen that higher accuracy rates were achieved than pre-trained models.

show abstract

A self-adaptive multi-objective feature selection approach for classification problems

Cited by 21 publications

References 89 publications

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

Knowledge-based decision intelligence in street lighting management

Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning

Contact Info

Product

Resources

About