Feature selection algorithm for hierarchical text classification using Kullback-Leibler divergence

Lifang, Yao; Qin, Sijun; Zhu, Hao

doi:10.1109/icccbda.2017.7951950

Cited by 6 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The input image is decomposed into 12 wavelet bands, i.e., three scales and four orientations. The six wavelet bands are randomly selected to extract features and minimize KLD [75], rendering a quality score to rate images in different distortion levels. The architecture of the proposed deployment scheme is given in Figure 5(d).…”

Section: Local and Global Features Methodsmentioning

confidence: 99%

Image Aesthetic Assessment: A Comparative Study of Hand-Crafted & Deep Learning Models

et al. 2022

View full text Add to dashboard Cite

Automatic image aesthetics assessment is a computer vision problem dealing with categorizing images into different aesthetic levels. The categorization is usually done by analyzing an input image and computing some measure of the degree to which the image adheres to the fundamental principles of photography such as balance, rhythm, harmony, contrast, unity, look, feel, tone, and texture. Due to its diverse applications in many areas, automatic image aesthetic assessment has gained significant research attention in recent years. This article presents a comparative study of different automatic image aesthetics assessment techniques from the year 2005 to 2021. A number of conventional hand-crafted as well as modern deep learning-based approaches are reviewed and analyzed for their performance on various publicly available datasets. Additionally, critical aspects of different features and models have also been discussed to analyze their performance and limitations in different situations. The comparative analysis reveals that deep learning based approaches excel hand-crafted based techniques in image aesthetic assessment.

show abstract

Section: Local and Global Features Methodsmentioning

confidence: 99%

Image Aesthetic Assessment: A Comparative Study of Hand-Crafted & Deep Learning Models

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The Kullback-Leibler divergence (KLD) is another measure often used as a feature evaluation tool [24], [37]- [40]. It is defined as [41]:…”

Section: G the Coefkld Coefficientmentioning

confidence: 99%

Empirical Comparison of the Feature Evaluation Methods Based on Statistical Measures

Łysiak

Szmajda

2021

IEEE Access

View full text Add to dashboard Cite

One of the most important classification problems is selecting proper features, i.e. features that describe the classified object in the most straightforward way possible. Then, one of the biggest challenges of the feature selection is the evaluation of the feature's quality. There is a plethora of feature evaluation methods in the literature. This paper presents the results of a comparison between nine selected feature evaluation methods, both existing in literature and newly defined. To make a comparison, features from ten various sets were evaluated by every method. Then, from every feature set, best subset (according to each method) was chosen. Those subsets then were used to train a set of classifiers (including decision trees and forests, linear discriminant analysis, naive Bayes, support vector machines, k nearest neighbors and an artificial neural network). The maximum accuracy of those classifiers, as well as the standard deviation between their accuracies, were used as a quality measures of each particular method. Furthermore, it was determined, which method is the most universal in terms of the data set, i.e. for which method, obtained accuracies were dependent on the feature set the least. Finally, computation time of each method was compared. Results indicated that for applications with limited computational power, method based on the average overlap between feature's values seem best suited. It led to high accuracies and proved to be fast to compute. However, if the data set is known to be normally distributed, method based on two-sample t-test may be preferable.

show abstract

“…Schneider [28], Wang et al [32], Zhang et al [34], and Lee et al [19] used the Kullback-Leibler divergence function to improve FS performance. Lifang et al [21] proposed a hierarchical class correlation feature selection method based on Kullback-Leibler divergence. Jiang et al [15] Developed a hybrid feature selection method based on Kullback-Leibler divergence methods.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection Based on Divergence Functions: A Comparative Classiffication Study

Pourmand

Shabbak

Ganjali

2021

Stat., optim. inf. comput.

View full text Add to dashboard Cite

Due to the extensive use of high-dimensional data and its application in a wide range of scientifc felds of research, dimensionality reduction has become a major part of the preprocessing step in machine learning. Feature selection is one procedure for reducing dimensionality. In this process, instead of using the whole set of features, a subset is selected to be used in the learning model. Feature selection (FS) methods are divided into three main categories: flters, wrappers, and embedded approaches. Filter methods only depend on the characteristics of the data, and do not rely on the learning model at hand. Divergence functions as measures of evaluating the differences between probability distribution functions can be used as flter methods of feature selection. In this paper, the performances of a few divergence functions such as Jensen-Shannon (JS) divergence and Exponential divergence (EXP) are compared with those of some of the most-known flter feature selection methods such as Information Gain (IG) and Chi-Squared (CHI). This comparison was made through accuracy rate and F1-score of classifcation models after implementing these feature selection methods.

show abstract

Feature selection algorithm for hierarchical text classification using Kullback-Leibler divergence

Cited by 6 publications

References 7 publications

Image Aesthetic Assessment: A Comparative Study of Hand-Crafted & Deep Learning Models

Image Aesthetic Assessment: A Comparative Study of Hand-Crafted & Deep Learning Models

Empirical Comparison of the Feature Evaluation Methods Based on Statistical Measures

Feature Selection Based on Divergence Functions: A Comparative Classiffication Study

Contact Info

Product

Resources

About