A Hybrid Instance Selection Method Based on Convex Hull and Nearest Neighbor

IE&EM 2019 ◽  
2020 ◽  
pp. 3-13
Author(s):  
Shanghe Wang ◽  
Jin Tian
Keyword(s):  
Convex Hull ◽  
Nearest Neighbor ◽  
Selection Method ◽  
Instance Selection

Feature Selection Method for Hydraulic System Faults Diagnosis Based on GA-PLS

2010 ◽  
Vol 44-47 ◽  
pp. 1130-1134
Author(s):  
Sheng Li ◽  
Pei Lin Zhang ◽  
Bing Li
Keyword(s):  
Feature Selection ◽  
Hydraulic System ◽  
Nearest Neighbor ◽  
Feature Selection Method ◽  
Original Data ◽  
Selection Method ◽  
Classification Model ◽  
K Nearest Neighbor ◽  
K Nearest Neighbor Algorithm ◽  
Faults Diagnosis

Feature selection is a key step in hydraulic system fault diagnosis. Some of the collected features are unrelated to classification model, and some are high correlated to other features. These features are harmful for establishing classification model. In order to solve this problem, genetic algorithm-partial least squares (GA-PLS) is proposed for selecting the representative and optimal features. K nearest neighbor algorithm (KNN) is used for diagnosing and classifying hydraulic system faults. For expressing better performance of GA-PLS, the original data of a model engineering hydraulic system is used, and the results of GA-PLS are compared with all feature used and GA. The experimental results show that, the proposed feature method can diagnose and classify hydraulic system faults more efficiently with using fewer features.

Improving the Performance of an Associative Classifier by Gamma Rough Sets Based Instance Selection

2017 ◽  
Vol 32 (01) ◽  
pp. 1860009 ◽  
Author(s):  
Jarvin A. Antón-Vargas ◽  
Yenny Villuendas-Rey ◽  
Cornelio Yáñez-Márquez ◽  
Itzamá López-Yáñez ◽  
Oscar Camacho-Nieto
Keyword(s):  
Information Systems ◽  
Rough Sets ◽  
Nearest Neighbor ◽  
Management Information Systems ◽  
Computational Cost ◽  
Instance Selection ◽  
Management Information ◽  
Similarity Relations ◽  
The Universe ◽  
Selection Of

This paper introduces the Gamma Rough Sets for management information systems where the universe objects are represented by continuous attributes and are connected by similarity relations. Some properties of such sets are demonstrated in this paper. In addition, Gamma Rough Sets are used to improve the Gamma associative classifier, by selecting instances. The results indicate that the selection of instances significantly reduces the computational cost of the Gamma classifier without affecting its effectiveness. The results also suggest that the selection of instances using Gamma Rough Sets favors other lazy learners, such as Nearest Neighbor and ALVOT.

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