Entropy based fuzzy classification of images on quality assessment

De, Indrajit; Sil, Jaya

doi:10.1016/j.jksuci.2012.05.001

Cited by 10 publications

(4 citation statements)

References 23 publications

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“…Another major application area for classification especially in information retrieval systems includes image classification (De and Sil, 2012). In this specific paper, authors used fuzzy logic to assign soft class labels to the different images in the collected dataset.…”

Section: Ontology Classification Of Email Contentsmentioning

confidence: 99%

Clustering and classification of email contents

Alsmadi

Alhami

2015

Journal of King Saud University - Computer and Information Scie

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Information users depend heavily on emails' system as one of the major sources of communication. Its importance and usage are continuously growing despite the evolution of mobile applications, social networks, etc. Emails are used on both the personal and professional levels. They can be considered as official documents in communication among users. Emails' data mining and analysis can be conducted for several purposes such as: Spam detection and classification, subject classification, etc. In this paper, a large set of personal emails is used for the purpose of folder and subject classifications. Algorithms are developed to perform clustering and classification for this large text collection. Classification based on NGram is shown to be the best for such large text collection especially as text is Bi-language (i.e. with English and Arabic content). Ó 2014 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Section: Ontology Classification Of Email Contentsmentioning

confidence: 99%

Clustering and classification of email contents

Alsmadi

Alhami

2015

Journal of King Saud University - Computer and Information Scie

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“…On the one hand, the method applies the entropy to measure the distribution of samples. In information theory, entropy is a measurement of uncertainty 31 . Samples closer to the margin of positive and negative classes always have lower‐class certainty.…”

Section: Introductionmentioning

confidence: 99%

“…In information theory, entropy is a measurement of uncertainty. 31 Samples closer to the margin of positive and negative classes always have lower-class certainty. Therefore, their entropies will be different with those samples farther to the margin.…”

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confidence: 99%

Entropy‐based hybrid sampling ensemble learning for imbalanced data

Chi

Wang

et al. 2021

Int J Intell Syst

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Sampling method is one of the most commonly used techniques in dealing with imbalanced data. Most of the existing undersampling methods randomly select samples from negative class with replacement. However, it may lose some important information of the training data. Moreover, increasing the positive data by oversampling in high imbalanced situations may cause the overlapping problem. To overcome these problems, this paper proposes a hybrid sampling method. The method takes the distributions of the training data into consideration by the information entropy, thus distinguishing the important samples in the undersampling procedure. Meanwhile, since the positive data only extend to the size of each subset of the negative class in the oversampling, the overlapping problem is relieved.Further, the method retains all the data in the training procedure and generates various data views from the original training data. Then each view is handled with an individual basic classifier. Finally, all the basic classifiers are combined by the ensemble method. The newly proposed method is named as Entropy-based Hybrid Sampling Ensemble Learning (EHSEL). In addition, the EHSEL is applied to three different kinds of basic classifiers to validate its robustness. Experiments results show the great effectiveness of the EHSEL on real-world imbalanced data sets.

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“…1, where subjective evaluation strategy of human beings are modeled with the help of salient features of the images. Type-1 fuzzy logic based inference system and fuzzy relational classifier [36] have already been built [40] considering human perceptual vagueness [38] in assessing quality of images using linguistic values. However, subjective judgment by human observers about quality of image is associated with uncertainty in input (feature) and output (quality class label) space for which an exact membership function is difficult to design.…”

Section: Introductionmentioning

confidence: 99%