Application of linear graph embedding as a dimensionality reduction technique and sparse representation classifier as a post classifier for the classification of epilepsy risk levels from EEG signals

2015 ◽  
Author(s):  
Sunil Kumar Prabhakar ◽  
Harikumar Rajaguru
Keyword(s):  
Dimensionality Reduction ◽  
Sparse Representation ◽  
Graph Embedding ◽  
Reduction Technique ◽  
Eeg Signals ◽  
Risk Levels ◽  
Linear Graph ◽  
Dimensionality Reduction Technique ◽  
Sparse Representation Classifier

scholarly journals An Analysis on Cervical Cancer Classification of Medical Digital Images Using Various Classifiers

2019 ◽  
pp. 98-102
Author(s):  
M. Robinson Joel ◽  
G. Vishali ◽  
R. Ponlatha ◽  
Syed Sharmila Begum
Keyword(s):  
Cervical Cancer ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Digital Images ◽  
Great Accuracy ◽  
Reduction Technique ◽  
Gynecological Malignancy ◽  
Dimensionality Reduction Technique ◽  
The World

In this analysis, Cervical cancer took over the place four in the world level and it is the most prevalent cancer that is affecting women. If the cancer is detected in the earlier stages it can be cured and treated successfully. And it is also the leading gynecological malignancy disease worldwide. This is a paper which presents the classification techniques of cervical cancer. And also, this paper shows the advanced feature solution approaches of cervical cancer. The dimensionality reduction technique is used for the improvement of the classifier with great accuracy. There are two categories of feature selection and they are filters and wrappers. By using all these analytic techniques, we can classify cancer and its approaches. Therefore, this paper classifies the approaches of Cervical cancer.

Band Selection Oriented to Easy-Confused Objects for Classification of Hyperspectral Imagery

2012 ◽  
Vol 500 ◽  
pp. 355-361
Author(s):  
Xin Zhao ◽  
Xing Li
Keyword(s):  
Dimensionality Reduction ◽  
Reduction Technique ◽  
Band Selection ◽  
Bhattacharyya Distance ◽  
Hyper Spectral ◽  
Processing Step ◽  
Dimensionality Reduction Technique ◽  
Selection Approach ◽  
Confusion Index

As a dimensionality reduction technique, band selection is an importance pre-processing step for classifiers. In this paper, a band selection approach oriented to easy-confused objects for classification of hyper spectral imagery is presented. Firstly, an Objects Confusion Index (OCI) is established to ascertain the easy-confused objects. Then the two band selection schemes, that are two-class mode and multi-class mode, are designed by adopting Bhattacharyya distance as class reparability measure.

scholarly journals ASSESSMENT OF EPILEPSY CLASSIFICATION USING TECHNIQUES SUCH AS SINGULAR VALUE DECOMPOSITION, APPROXIMATE ENTROPY, AND WEIGHTED K-NEAREST NEIGHBORS MEASURES

2016 ◽  
Vol 9 (5) ◽  
pp. 91 ◽  
Author(s):  
Sunil Kumar P ◽  
Harikumar Rajaguru
Keyword(s):  
Singular Value Decomposition ◽  
Dimensionality Reduction ◽  
Neurological Diseases ◽  
Approximate Entropy ◽  
Singular Value ◽  
Eeg Signals ◽  
Risk Levels ◽  
Value Decomposition ◽  
The Brain

ABSTRACTObjective: The main aim of this research is to reduce the dimension of the epileptic Electroencephalography (EEG) signals and then classify it usingvarious post classifiers. For the evaluation and easy treatment of neurological diseases, EEG signals are used. The reflection of the electrical activitiesof the human brain is obtained by the measurement of potentials in EEG. To study and explore the brain functions in an exhaustive manner, EEG is usedby both physicians and scientists. The study of the electrical activity of the brain which is done through EEG recording is a vital tool for the diagnosis ofmany neurological diseases which include epilepsy, sleep disorders, injuries in head, dementia etc. One of the most commonly occurring and prevalentneurological disorders is epilepsy and it is easily characterized by recurrent seizures.Methods: This paper employs the concept of dimensionality reduction concepts like Fuzzy Mutual Information (FMI), Independent ComponentAnalysis (ICA), Linear Graph Embedding (LGE), Linear Discriminant Analysis (LDA) and finally Variational Bayesian Matrix Factorization (VBMF).The epilepsy risk levels are also classified using post classifiers like Singular Value Decomposition (SVD), Approximate Entropy (ApEn) and WeightedKNN (W-KNN) classifiers.Results: The highest accuracy is obtained when LDA is combined with Weighted KNN (W-KNN) Classifiers and it is of 97.18%. Conclusion: Thus the EEG signals not only represent the brain function but also the status of the whole body. The best result obtained was whenLDA is engaged as a dimensionality reduction technique followed by the usage of the W-KNN as post classifier for the classification of epilepsy risklevels from EEG signals. Future work may incorporate the possible usage of different dimensionality reduction techniques with various other types ofclassifiers for the perfect classification of epilepsy risk levels from EEG signals.Keywords: FMI, ICA, LGE, LDA, W-KNN, EEG

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