Penalty-Reward Based Instance Selection Method in Cloud Environment Using the Concept of Nearest Neighbor

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.

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Simulation Model Selection Method Based on Semantic Search in Cloud Environment

Communications in Computer and Information Science - Methods and Applications for Modeling and Simulation of Complex Systems ◽

10.1007/978-981-15-1078-6_19 ◽

2019 ◽

pp. 222-233

Author(s):

Siqi Xiong ◽

Feng Zhu ◽

Yiping Yao ◽

Wenjie Tang

Keyword(s):

Model Selection ◽

Simulation Model ◽

Selection Method ◽

Semantic Search ◽

Cloud Environment ◽

Model Selection Method

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A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction

Journal of Computer Science and Technology ◽

10.1007/s11390-015-1575-5 ◽

2015 ◽

Vol 30 (5) ◽

pp. 969-980 ◽

Cited By ~ 37

Author(s):

Duksan Ryu ◽

Jong-In Jang ◽

Jongmoon Baik

Keyword(s):

Nearest Neighbor ◽

Defect Prediction ◽

Instance Selection ◽

Cross Project

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

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001418600091 ◽

2017 ◽

Vol 32 (01) ◽

pp. 1860009 ◽

Cited By ~ 4

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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