Auto Encoder Fixed-Target Training Features Extraction Approach for Binary Classification Problems - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue (Data Paper) Asian Journal of Research in Computer Science Année : 2023

Auto Encoder Fixed-Target Training Features Extraction Approach for Binary Classification Problems

Résumé

The main issues with machine learning-based feature extraction techniques are the requirement of extensive domain-level knowledge, experience, and the need to be supported by large amounts of data that are sometimes not available. Moreover, it is often difficult to apply domain-level knowledge to extract the necessary features for building a machine-learning classifier. Therefore, it is significantly important to find and develop feature extraction techniques that depend mainly on the training data and don’t require or depend on domain-level knowledge and experience. To address these issues for binary classification problems, a novel feature extraction approach, AE-FT(Fixed Target) for extracting common features using a Deep Belief Network (DBN)-based Autoencoder (AE) is proposed in this paper. In this approach, common features are extracted by a DBN trained on a dataset sample’s binary using the Fixed Target training approach. The proposed common features extraction approach is tested and evaluated on two different data sets. For each dataset, the extracted features are used to train seven of the common machine learning binary classification algorithms and compared their performances. Moreover, the number of extracted features is very small compared to other existing feature extraction methods. Therefore, the proposed common features extraction method improves the performance of the binary classification algorithms by reducing the number of features reducing laborious processes, and increasing the recognition accuracy effectively. The results show that the proposed common features extraction approach, without any domain-level knowledge or human expertise, provides a very good performance compared to other feature extraction techniques.
Fichier principal
Vignette du fichier
Alkhateem1512023AJRCOS96169.pdf (710.82 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Licence : ETALAB - Licence Ouverte

Dates et versions

hal-03991744 , version 1 (16-02-2023)

Licence

Licence Ouverte - etalab

Identifiants

Citer

Yasir N. S. Alkhateem, M. Mejri. Auto Encoder Fixed-Target Training Features Extraction Approach for Binary Classification Problems. Asian Journal of Research in Computer Science, 2023, pp.32-43. ⟨10.9734/ajrcos/2023/v15i1313⟩. ⟨hal-03991744⟩
3 Consultations
13 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More