Neural networks for classification problem on tabular data

Abstract This work presents the results of using self-normalizing neural networks with automatic selection of hyperparameters, TabNet and NODE to solve the problem of tabular data classification. The method of automatic selection of hyperparameters was realised. Testing was carried out with the open source framework OpenML AutoML Benchmark. As part of the work, a comparative analysis was carried out with seven classification methods, experiments were carried out for 39 datasets with 5 methods. NODE shows the best results among the following methods and overperformed standard methods for four datasets.

Download Full-text

Automatic selection of hidden neurons and weights in neural networks for data classification using hybrid particle swarm optimization, multi-verse optimization based on Lévy flight

Evolutionary Intelligence ◽

10.1007/s12065-021-00579-w ◽

2021 ◽

Author(s):

Rabab Bousmaha ◽

Reda Mohamed Hamou ◽

Abdelmalek Amine

Keyword(s):

Neural Networks ◽

Particle Swarm Optimization ◽

Particle Swarm ◽

Data Classification ◽

Swarm Optimization ◽

Hybrid Particle ◽

Hybrid Particle Swarm Optimization ◽

Automatic Selection ◽

Hidden Neurons ◽

Selection Of

Download Full-text

Features Extraction and Classification Methods for Automatic Selection of Two Different Types of Paint for Automotive Parts

10.1364/fio.2021.jth5a.46 ◽

2021 ◽

Author(s):

Deborah Martínez ◽

Rafael Guzmán-Cabrera ◽

Daniel A. May-Arrioja ◽

Iván Hernández-Romano ◽

Miguel Torres-Cisneros

Keyword(s):

Features Extraction ◽

Classification Methods ◽

Automatic Selection ◽

Different Types ◽

Automotive Parts ◽

Selection Of

Download Full-text

Open-Source Neural Architecture Search with Ensemble and Pre-trained Networks

International Journal of Modeling and Optimization ◽

10.7763/ijmo.2021.v11.774 ◽

2021 ◽

pp. 33-41

Author(s):

Séamus Lankford ◽

◽

Diarmuid Grimes

Keyword(s):

Neural Networks ◽

Image Classification ◽

Open Source ◽

Classification Models ◽

Classification Problems ◽

Neural Architecture ◽

Ensemble Performance ◽

Heterogeneous Models ◽

Rgb Images ◽

Selection Of

The training and optimization of neural networks, using pre-trained, super learner and ensemble approaches is explored. Neural networks, and in particular Convolutional Neural Networks (CNNs), are often optimized using default parameters. Neural Architecture Search (NAS) enables multiple architectures to be evaluated prior to selection of the optimal architecture. Our contribution is to develop, and make available to the community, a system that integrates open source tools for the neural architecture search (OpenNAS) of image classification models. OpenNAS takes any dataset of grayscale, or RGB images, and generates the optimal CNN architecture. Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and pre-trained models serve as base learners for ensembles. Meta learner algorithms are subsequently applied to these base learners and the ensemble performance on image classification problems is evaluated. Our results show that a stacked generalization ensemble of heterogeneous models is the most effective approach to image classification within OpenNAS.

Download Full-text