Text stream clustering algorithm based on adaptive feature selection

2011 ◽  
Vol 38 (3) ◽  
pp. 1393-1399 ◽  
Author(s):  
Linghui Gong ◽  
Jianping Zeng ◽  
Shiyong Zhang
Keyword(s):  
Feature Selection ◽  
Clustering Algorithm ◽  
Stream Clustering ◽  
Adaptive Feature Selection

scholarly journals TPK: a single-cell clustering algorithm based on novel feature selection genes

2021 ◽  
Vol 1738 ◽  
pp. 012078
Author(s):  
Yaxuan Cui ◽  
Kunjie Luo ◽  
Zheyu Zhang ◽  
Saijia Liu
Keyword(s):  
Feature Selection ◽  
Single Cell ◽  
Clustering Algorithm ◽  
Cell Clustering

scholarly journals A cluster-based feature selection method for image texture classification

2019 ◽  
Vol 14 (3) ◽  
pp. 1433 ◽  
Author(s):  
Abbas F. H. Alharan ◽  
Hayder K. Fatlawi ◽  
Nabeel Salih Ali
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Clustering Algorithm ◽  
Gabor Filter ◽  
Texture Feature ◽  
Image Feature ◽  
Image Texture ◽  
Support Vector ◽  
Texture Image ◽  
Texture Feature Extraction

<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>

Adaptive Feature Selection Siamese Networks for Visual Tracking

2020 ◽  
pp. 167-179
Author(s):  
Mustansar Fiaz ◽  
Md. Maklachur Rahman ◽  
Arif Mahmood ◽  
Sehar Shahzad Farooq ◽  
Ki Yeol Baek ◽  
...  
Keyword(s):  
Feature Selection ◽  
Visual Tracking ◽  
Adaptive Feature Selection ◽  
Siamese Networks

Multi-Scale Correlation Filtering Tracker Based on Adaptive Feature Selection

2017 ◽  
Vol 37 (5) ◽  
pp. 0515001 ◽  
Author(s):  
沈 秋 Shen Qiu ◽  
严小乐 Yan Xiaole ◽  
刘霖枫 Liu Linfeng ◽  
孔繁锵 Kong Fanqiang ◽  
王丹丹 Wang Dandan
Keyword(s):  
Feature Selection ◽  
Multi Scale ◽  
Adaptive Feature Selection ◽  
Correlation Filtering
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