Latent‐lSVM classification of very high‐dimensional and large‐scale multi‐class datasets

2017 ◽  
Vol 31 (2) ◽  
pp. e4224 ◽  
Author(s):  
Thanh‐Nghi Do ◽  
François Poulet
Keyword(s):  
Large Scale ◽  
High Dimensional ◽  
Very High

Weighted Similarity Classifier Using Differential Evolution and Genetic Algorithm in Weight Optimization

2004 ◽  
Vol 8 (6) ◽  
pp. 591-598 ◽  
Author(s):  
Pasi Luukka ◽  
◽  
Jouni Sampo
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Similarity Measures ◽  
High Dimensional ◽  
Weight Optimization ◽  
Large Scale Data ◽  
Data Weighting

We have compared the differential evolution and genetic algorithms in a study of weight optimization for different similarity measures in a task of classification. In a study of high dimensional data weighting similarity measures become of great importance and efforts to study suitable optimizers is needed. In this article we have studied proper weighting of similarity measures in the classification of high dimensional and large scale data. We will show that in most cases the differential evolution algorithm should be used in finding the weights instead of the genetic algorithm.

scholarly journals A Dynamic Ensemble Framework for Mining Textual Streams with Class Imbalance

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Ge Song ◽  
Yunming Ye
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Concept Drift ◽  
Real Life ◽  
Class Imbalance ◽  
High Dimensional ◽  
Adaptive Selection ◽  
Stream Classification ◽  
Rare Class

Textual stream classification has become a realistic and challenging issue since large-scale, high-dimensional, and non-stationary streams with class imbalance have been widely used in various real-life applications. According to the characters of textual streams, it is technically difficult to deal with the classification of textual stream, especially in imbalanced environment. In this paper, we propose a new ensemble framework, clustering forest, for learning from the textual imbalanced stream with concept drift (CFIM). The CFIM is based on ensemble learning by integrating a set of clustering trees (CTs). An adaptive selection method, which flexibly chooses the useful CTs by the property of the stream, is presented in CFIM. In particular, to deal with the problem of class imbalance, we collect and reuse both rare-class instances and misclassified instances from the historical chunks. Compared to most existing approaches, it is worth pointing out that our approach assumes that both majority class and rareclass may suffer from concept drift. Thus the distribution of resampled instances is similar to the current concept. The effectiveness of CFIM is examined in five real-world textual streams under an imbalanced nonstationary environment. Experimental results demonstrate that CFIM achieves better performance than four state-of-the-art ensemble models.

scholarly journals High Dimensional Classification of Structural MRI Alzheimer?s Disease Data Based on Large Scale Regularization

2011 ◽  
Vol 5 ◽  
Author(s):  
Ramon Casanova ◽  
Christopher T. Whitlow ◽  
Benjamin Wagner ◽  
Jeff Williamson ◽  
Sally A. Shumaker ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Mri ◽  
High Dimensional ◽  
Dimensional Classification

scholarly journals Creating large-scale maps for agriculture using remote sensing

2021 ◽  
Vol 227 ◽  
pp. 03002
Author(s):  
Gayrat Yakubov ◽  
Khamid Mubarakov ◽  
Ilkhomjon Abdullaev ◽  
Azizjon Ruziyev
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Land Resources ◽  
Agricultural Lands ◽  
Very High Spatial Resolution ◽  
Very High ◽  
Real State

Reliable information on the real state of agricultural lands will be required to the development of appropriate measures for the rational use of agricultural lands. To obtain such information, it is necessary to keep permanent and systematic records and inventories of land resources. Large-scale special plans and maps will be required for accounting, inventory and classification of agricultural land. Currently in Uzbekistan such cartographic materials are being created on the scale 1: 10 000 and 1: 25 000 by administrative and territorial units, farms or individual land plots. The article considers the issues of creation of special maps of agricultural land in scale 1:10000 on the example of Sharof Rashidov district of Jizzakh region using remote sensing data with very high spatial resolution KOMPSAT-3.

Classification Of Very High Dimensional Data Using Neural Networks

2005 ◽  
Author(s):  
J.A. Benediktsson ◽  
P.H. Swain ◽  
O.K. Ersoy ◽  
D. Hong
Keyword(s):  
Neural Networks ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Very High
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