More About Asymptotic Properties of Some Binary Classification Methods for High Dimensional Data

2021 ◽  
pp. 43-57
Author(s):  
Addy Bolivar-Cime
Keyword(s):  
Binary Classification ◽  
Asymptotic Properties ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Classification Methods

1D embedding multi-category classification methods

2016 ◽  
Vol 14 (02) ◽  
pp. 1640006 ◽  
Author(s):  
Luoqing Li ◽  
Chuanwu Yang ◽  
Qiwei Xie
Keyword(s):  
Binary Classification ◽  
High Dimensional Data ◽  
Classification Performance ◽  
Classification Method ◽  
High Dimensional ◽  
Classification Methods ◽  
One Dimensional ◽  
Interpolation Technique ◽  
Facial Images

In this paper, we propose a novel semi-supervised multi-category classification method based on one-dimensional (1D) multi-embedding. Based on the multiple 1D embedding based interpolation technique, we embed the high-dimensional data into several different 1D manifolds and perform binary classification firstly. Then we construct the multi-category classifiers by means of one-versus-rest and one-versus-one strategies separately. A weight strategy is employed in our algorithm for improving the classification performance. The proposed method shows promising results in the classification of handwritten digits and facial images.

scholarly journals A Method for Entity Resolution in High Dimensional Data Using Ensemble Classifiers

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Liu Yi ◽  
Diao Xing-chun ◽  
Cao Jian-jun ◽  
Zhou Xing ◽  
Shang Yu-ling
Keyword(s):  
Binary Classification ◽  
High Dimensional Data ◽  
Entity Resolution ◽  
Classification Problem ◽  
High Dimensional ◽  
Utilization Rate ◽  
Ensemble Classifiers ◽  
Ant Colony Optimization Algorithm ◽  
Base Classifier ◽  
Set Up

In order to improve utilization rate of high dimensional data features, an ensemble learning method based on feature selection for entity resolution is developed. Entity resolution is regarded as a binary classification problem, an optimization model is designed to maximize each classifier’s classification accuracy and dissimilarity between classifiers and minimize cardinality of features. A modified multiobjective ant colony optimization algorithm is employed to solve the model for each base classifier, two pheromone matrices are set up, weighted product method is applied to aggregate values of two pheromone matrices, and feature’s Fisher discriminant rate of records’ similarity vector is calculated as heuristic information. A solution which is called complementary subset is selected from Pareto archive according to the descending order of three objectives to train the given base classifier. After training all base classifiers, their classification outputs are aggregated by max-wins voting method to obtain the ensemble classifiers’ final result. A simulation experiment is carried out on three classical datasets. The results show the effectiveness of our method, as well as a better performance compared with the other two methods.

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional
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