Wavelet transform and SGLDM: a classification performance study using ML parameter estimation, minimum distance, and k-nearest-neighbor classifiers

1997 ◽  
Author(s):  
Reena Singh ◽  
Ramon E. Vasquez ◽  
Rajeev Singh
Keyword(s):  
Parameter Estimation ◽  
Wavelet Transform ◽  
Minimum Distance ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Performance Study ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classifiers

A Comparison of the Analysis of Methods for Feature Extraction and Classification by Wavelet Transform in SSVEP BCIs

2020 ◽  
Author(s):  
Hoda Heidari ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Hamidreza Hosseinzadeh
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Iir Filters ◽  
Wide Range ◽  
New Feature

Abstract Most of the studies in the field of Brain-Computer Interface (BCI) based on electroencephalography have a wide range of applications. Extracting Steady State Visual Evoked Potential (SSVEP) is regarded as one of the most useful tools in BCI systems. In this study, different methods such as feature extraction with different spectral methods (Shannon entropy, skewness, kurtosis, mean, variance) (bank of filters, narrow-bank IIR filters, and wavelet transform magnitude), feature selection performed by various methods (decision tree, principle component analysis (PCA), t-test, Wilcoxon, Receiver operating characteristic (ROC)), and classification step applying k nearest neighbor (k-NN), perceptron, support vector machines (SVM), Bayesian, multiple layer perceptron (MLP) were compared from the whole stream of signal processing. Through combining such methods, the effective overview of the study indicated the accuracy of classical methods. In addition, the present study relied on a rather new feature selection described by decision tree and PCA, which is used for the BCI-SSVEP systems. Finally, the obtained accuracies were calculated based on the four recorded frequencies representing four directions including right, left, up, and down.

Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers

2010 ◽  
Vol 5 (2) ◽  
pp. 133-137 ◽  
Author(s):  
Mohammed J. Islam ◽  
Q. M. Jonathan Wu ◽  
Majid Ahmadi ◽  
Maher A. SidAhmed
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classifiers

scholarly journals A New Approach to Fall Detection Based on Improved Dual Parallel Channels Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2814 ◽  
Author(s):  
Xiaoguang Liu ◽  
Huanliang Li ◽  
Cunguang Lou ◽  
Tie Liang ◽  
Xiuling Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Fall Detection ◽  
Classification Performance ◽  
Daily Activities ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Parallel Channels

Falls are the major cause of fatal and non-fatal injury among people aged more than 65 years. Due to the grave consequences of the occurrence of falls, it is necessary to conduct thorough research on falls. This paper presents a method for the study of fall detection using surface electromyography (sEMG) based on an improved dual parallel channels convolutional neural network (IDPC-CNN). The proposed IDPC-CNN model is designed to identify falls from daily activities using the spectral features of sEMG. Firstly, the classification accuracy of time domain features and spectrograms are compared using linear discriminant analysis (LDA), k-nearest neighbor (KNN) and support vector machine (SVM). Results show that spectrograms provide a richer way to extract pattern information and better classification performance. Therefore, the spectrogram features of sEMG are selected as the input of IDPC-CNN to distinguish between daily activities and falls. Finally, The IDPC-CNN is compared with SVM and three different structure CNNs under the same conditions. Experimental results show that the proposed IDPC-CNN achieves 92.55% accuracy, 95.71% sensitivity and 91.7% specificity. Overall, The IDPC-CNN is more effective than the comparison in accuracy, efficiency, training and generalization.

scholarly journals Speculate-correct error bounds for k-nearest neighbor classifiers

2019 ◽  
Vol 108 (12) ◽  
pp. 2087-2111 ◽  
Author(s):  
Eric Bax ◽  
Lingjie Weng ◽  
Xu Tian
Keyword(s):  
Error Bounds ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classifiers

Comparative analysis of breast cancer detection in mammograms and thermograms

2015 ◽  
Vol 60 (1) ◽  
Author(s):  
Marina Milosevic ◽  
Dragan Jankovic ◽  
Aleksandar Peulic
Keyword(s):  
Nearest Neighbor ◽  
Region Of Interest ◽  
Texture Features ◽  
Classification Performance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Characteristic Analysis ◽  
Analysis Society ◽  
Fold Cross Validation ◽  
Neighbor Classifier

AbstractIn this paper, we present a system based on feature extraction techniques for detecting abnormal patterns in digital mammograms and thermograms. A comparative study of texture-analysis methods is performed for three image groups: mammograms from the Mammographic Image Analysis Society mammographic database; digital mammograms from the local database; and thermography images of the breast. Also, we present a procedure for the automatic separation of the breast region from the mammograms. Computed features based on gray-level co-occurrence matrices are used to evaluate the effectiveness of textural information possessed by mass regions. A total of 20 texture features are extracted from the region of interest. The ability of feature set in differentiating abnormal from normal tissue is investigated using a support vector machine classifier, Naive Bayes classifier and K-Nearest Neighbor classifier. To evaluate the classification performance, five-fold cross-validation method and receiver operating characteristic analysis was performed.

scholarly journals WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 413 ◽  
Author(s):  
Chris Lytridis ◽  
Anna Lekova ◽  
Christos Bazinas ◽  
Michail Manios ◽  
Vassilis G. Kaburlasos
Keyword(s):  
Time Series ◽  
Ad Hoc ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Human Robot Interaction ◽  
Time Series Classification ◽  
K Nearest Neighbor ◽  
Time Dimension ◽  
Knn Classifier ◽  
Benchmark Datasets

Our interest is in time series classification regarding cyber–physical systems (CPSs) with emphasis in human-robot interaction. We propose an extension of the k nearest neighbor (kNN) classifier to time-series classification using intervals’ numbers (INs). More specifically, we partition a time-series into windows of equal length and from each window data we induce a distribution which is represented by an IN. This preserves the time dimension in the representation. All-order data statistics, represented by an IN, are employed implicitly as features; moreover, parametric non-linearities are introduced in order to tune the geometrical relationship (i.e., the distance) between signals and consequently tune classification performance. In conclusion, we introduce the windowed IN kNN (WINkNN) classifier whose application is demonstrated comparatively in two benchmark datasets regarding, first, electroencephalography (EEG) signals and, second, audio signals. The results by WINkNN are superior in both problems; in addition, no ad-hoc data preprocessing is required. Potential future work is discussed.

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