scholarly journals A distributed feature selection scheme with partial information sharing

2019 ◽  
Vol 108 (11) ◽  
pp. 2009-2034
Author(s):  
Aida Brankovic ◽  
Luigi Piroddi
Keyword(s):  
Feature Selection ◽  
Information Sharing ◽  
Partial Information ◽  
Selection Scheme

scholarly journals An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3627
Author(s):  
Bo Jin ◽  
Chunling Fu ◽  
Yong Jin ◽  
Wei Yang ◽  
Shengbin Li ◽  
...  
Keyword(s):  
Feature Selection ◽  
Local Structure ◽  
Gene Selection ◽  
Dimensional Space ◽  
Original Data ◽  
Global Structure ◽  
Biological Data ◽  
Special Treatment ◽  
Selection Scheme ◽  
Unsupervised Feature Selection

Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions. In recent years, unsupervised feature selection algorithms have attracted considerable attention in the field of gene selection as they can find the most discriminating subsets of genes, namely the potential information in biological data. Recent research also shows that maintaining the important structure of data is necessary for gene selection. However, most current feature selection methods merely capture the local structure of the original data while ignoring the importance of the global structure of the original data. We believe that the global structure and local structure of the original data are equally important, and so the selected genes should maintain the essential structure of the original data as far as possible. In this paper, we propose a new, adaptive, unsupervised feature selection scheme which not only reconstructs high-dimensional data into a low-dimensional space with the constraint of feature distance invariance but also employs ℓ2,1-norm to enable a matrix with the ability to perform gene selection embedding into the local manifold structure-learning framework. Moreover, an effective algorithm is developed to solve the optimization problem based on the proposed scheme. Comparative experiments with some classical schemes on real tumor datasets demonstrate the effectiveness of the proposed method.

A Distributed Integrated Feature Selection Scheme for Column Subset Selection

2021 ◽  
pp. 1-1
Author(s):  
Zheng Xiao ◽  
Pengcheng Wei ◽  
Anthony Chronopoulos ◽  
Anne C. Elster
Keyword(s):  
Feature Selection ◽  
Subset Selection ◽  
Selection Scheme ◽  
Column Subset Selection

MODELING THE IMPACT OF PARTIAL INFORMATION SHARING IN A THREE-ECHELON SUPPLY CHAIN

2013 ◽  
Vol 30 (05) ◽  
pp. 1350020 ◽  
Author(s):  
ZHUPING LIU ◽  
QIUHONG ZHAO ◽  
SHOUYANG WANG ◽  
JIANMING SHI
Keyword(s):  
Supply Chain ◽  
Numerical Analysis ◽  
Information Sharing ◽  
Partial Information ◽  
Autoregressive Coefficient ◽  
The Impact

This paper investigates the impact of partial information sharing in a three-echelon supply chain. Partial information sharing means that information sharing occurs only between the distributor and the retailer, but not between the distributor and the manufacturer. This paper contributes to the literature by summarizing the circumstances in which information sharing between the retailer and the distributor benefits the manufacturer. In addition, our study points out that such information sharing does not always bring benefits to the manufacturer and that in some cases the information sharing may harm the manufacturer. We explain the reasons why this can happen and give managerial intuition for our results. Using numerical analysis, we illustrate the impact of partial information sharing on the agents in the supply chain with the change of the autoregressive coefficient in the demand process.

scholarly journals VESSEL CLASSIFICATION IN COSMO-SKYMED SAR DATA USING HIERARCHICAL FEATURE SELECTION

2015 ◽  
Vol XL-7/W3 ◽  
pp. 975-982 ◽  
Author(s):  
A. Makedonas ◽  
C. Theoharatos ◽  
V. Tsagaris ◽  
V. Anastasopoulos ◽  
S. Costicoglou
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Selection Procedure ◽  
Texture Features ◽  
Statistical Characteristics ◽  
Selection Scheme ◽  
Feature Extraction And Selection ◽  
Sar Data ◽  
Ship Classification ◽  
Analysis Scale

SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features’ statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

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