Skip to content
Research Article Open access CC BY 4.0

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

Yasir N. S. Alkhateem, M. Mejri

Asian Journal of Research in Computer Science · pp. 32–43 · Published 30 Jan 2023

10.9734/ajrcos/2023/v15i1313

Abstract

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.

Features extraction deep learning AE fixed-target training common features

References (33)

  1. 1 ImageNet classification with deep convolutional neural networks [DOI]
  2. 2 Visualizing Data using t-SNE
  3. 3 Reducing the Dimensionality of Data with Neural Networks [DOI]
  4. 4 A Fast Learning Algorithm for Deep Belief Nets [DOI]
  5. 5 Eigenfaces vs. Fisherfaces: recognition using class specific linear projection [DOI]
  6. 6 Nonlinear Component Analysis as a Kernel Eigenvalue Problem [DOI]
  7. 7 Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion [DOI]
  8. 8 Learning Word Vectors for Sentiment Analysis
  9. 9 Learning Internal Representations by Error Propagation [DOI]
  10. 10 Building high-level features using large scale unsupervised learning [DOI]
  11. 11 Stochastic Neighbor Embedding
  12. 12 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
  13. 13 Sparse deep belief net model for visual area V2
  14. 14 Linear Discriminant Analysis [DOI]
  15. 15 Spam Filtering with Naive Bayes - Which Naive Bayes?
  16. 16 DeepSign: Deep learning for automatic malware signature generation and classification [DOI]
  17. 17 Dimension reduction of image deep feature using PCA [DOI]
  18. 18 Feature Extraction Using an RNN Autoencoder for Skeleton-Based Abnormal Gait Recognition [DOI]
  19. 19 m-SNE: Multiview Stochastic Neighbor Embedding [DOI]
  20. 20 Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification [DOI]
  21. 21 Review of Dimension Reduction Methods [DOI]
  22. 22 On the Naive Bayes Model for Text Categorization.
  23. 23 Weighted locally linear embedding for dimension reduction [DOI]
  24. 24 A new perspective to null linear discriminant analysis method and its fast implementation using random matrix multiplication with scatter matrices [DOI]
  25. 25 Semisupervised Generalized Discriminant Analysis [DOI]
  26. 26 Neural Architecture Search Net-Based Feature Extraction With Modular Neural Network for Image Classification of Copper/ Cobalt Raw Minerals [DOI]
  27. 27 An Expectation-Maximization Approach to Nonlinear Component Analysis [DOI]
  28. 28 Incremental Locally Linear Embedding Algorithm [DOI]
  29. 29 Combining text and heuristics for cost-sensitive spam filtering [DOI]
  30. 30 Greedy kernel PCA for training data reduction and nonlinear feature extraction in classification [DOI]
  31. 31 Very Deep Convolutional Networks for Large-Scale Image Recognition [DOI]
  32. 32 Learning Internal Representations by Error Propagation [DOI]
  33. 33 Special issue on feature engineering editorial [DOI]

Cited by 2

Improving Recommender Systems for Fake News Detection in Social Networks with Knowledge Graphs and Graph Attention Networks

A. Golovin, N. Zhukova, R. Delhibabu · Mathematics · 2025

Showing 1 of 2 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

13

Page views

0

PDF downloads

2

Outbound clicks

2

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.