scholarly journals DISCRIMINANT ANALYSIS THROUGH FISHER METHOD ON STUDENT'S MATERIALS BUILDING IN SMP 1 BANDA ACEH

2019 ◽  
Vol 2 (2) ◽  
pp. 301
Author(s):  
Y Yuhasriati ◽  
Johan Yunus ◽  
Erni Maidiyah ◽  
Bintang Zaura
Keyword(s):  
Discriminant Analysis ◽  
Discriminant Function ◽  
Analysis Method ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Fisher Method ◽  
Eighth Grade Students ◽  
Study Population ◽  
Class Viii ◽  
Banda Aceh

The study is done for determining how many students and how the percentage of students who have mastered the material geometry about prism and pyramid, and getting the discriminant function about the mastery of the material. The study population was the entire eighth grade students of SMP Kasih Banda Aceh while the sample were students of class VIII-1 and VIII-8 to 66 students. Based on the research that has been done using the fisher discriminant analysis method, 18,18% of students have mastered the material geometry and 81,82% of students have not mastered the material geometry. It means that from 66 students only 12 students were able to master the material geometry about well- prism and pyramid. The discriminant function that is formed is and .

Optimal multi-kernel local fisher discriminant analysis for feature dimensionality reduction and fault diagnosis

2021 ◽  
pp. 1748006X2110093
Author(s):  
Qing Zhang ◽  
Heng Li ◽  
Xiaolong Zhang ◽  
Haifeng Wang
Keyword(s):  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Feature Space ◽  
High Dimensional ◽  
Fisher Discriminant Analysis ◽  
Vibration Signals ◽  
Fisher Discriminant ◽  
Local Fisher Discriminant Analysis ◽  
Diagnosis Accuracy ◽  
Diagnosis Model

To achieve a more desirable fault diagnosis accuracy by applying multi-domain features of vibration signals, it is significative and challenging to refine the most representative and intrinsic feature components from the original high dimensional feature space. A novel dimensionality reduction method for fault diagnosis is proposed based on local Fisher discriminant analysis (LFDA) which takes both label information and local geometric structure of the high dimensional features into consideration. Multi-kernel trick is introduced into the LFDA to improve its performance in dealing with the nonlinearity of mapping high dimensional feature space into a lower one. To obtain an optimal diagnosis accuracy by the reduced features of low dimensionality, binary particle swarm optimization (BPSO) algorithm is utilized to search for the most appropriate parameters of kernels and K-nearest neighbor (kNN) recognition model. Samples with labels are used to train the optimal multi-kernel LFDA and kNN (OMKLFDA-kNN) fault diagnosis model to obtain the optimal transformation matrix. Consequently, the trained fault diagnosis model implements the recognition of machinery health condition with the most representative feature space of vibration signals. A bearing fault diagnosis experiment is conducted to verify the effectiveness of proposed diagnostic approach. Performance comparison with some other methods are investigated, and the improvement for fault diagnosis of the proposed method are confirmed in different aspects.

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