Bird Voice Classification Based on Combination Feature Extraction and Reduction Dimension with the K-Nearest Neighbor

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

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A Comparative Survey of Feature Extraction and Machine Learning Methods in Diverse Acoustic Environments

Sensors ◽

10.3390/s21041274 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1274

Author(s):

Daniel Bonet-Solà ◽

Rosa Ma Alsina-Pagès

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Best Practice ◽

Nearest Neighbor ◽

Gaussian Mixture ◽

Machine Learning Algorithms ◽

Multimedia Retrieval ◽

Natural Environments ◽

K Nearest Neighbor ◽

Acoustic Environments

Acoustic event detection and analysis has been widely developed in the last few years for its valuable application in monitoring elderly or dependant people, for surveillance issues, for multimedia retrieval, or even for biodiversity metrics in natural environments. For this purpose, sound source identification is a key issue to give a smart technological answer to all the aforementioned applications. Diverse types of sounds and variate environments, together with a number of challenges in terms of application, widen the choice of artificial intelligence algorithm proposal. This paper presents a comparative study on combining several feature extraction algorithms (Mel Frequency Cepstrum Coefficients (MFCC), Gammatone Cepstrum Coefficients (GTCC), and Narrow Band (NB)) with a group of machine learning algorithms (k-Nearest Neighbor (kNN), Neural Networks (NN), and Gaussian Mixture Model (GMM)), tested over five different acoustic environments. This work has the goal of detailing a best practice method and evaluate the reliability of this general-purpose algorithm for all the classes. Preliminary results show that most of the combinations of feature extraction and machine learning present acceptable results in most of the described corpora. Nevertheless, there is a combination that outperforms the others: the use of GTCC together with kNN, and its results are further analyzed for all the corpora.

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A novel content based image retrieval system using K-means/KNN with feature extraction

Computer Science and Information Systems ◽

10.2298/csis120122047c ◽

2012 ◽

Vol 9 (4) ◽

pp. 1645-1661 ◽

Cited By ~ 10

Author(s):

Ray-I Chang ◽

Shu-Yu Lin ◽

Jan-Ming Ho ◽

Chi-Wen Fann ◽

Yu-Chun Wang

Keyword(s):

Feature Extraction ◽

Image Retrieval ◽

Nearest Neighbor ◽

Clustering Algorithms ◽

Content Based Image Retrieval ◽

K Nearest Neighbor ◽

Color Analysis ◽

Image Retrieval System ◽

First Time ◽

System Designs

Image retrieval has been popular for several years. There are different system designs for content based image retrieval (CBIR) system. This paper propose a novel system architecture for CBIR system which combines techniques include content-based image and color analysis, as well as data mining techniques. To our best knowledge, this is the first time to propose segmentation and grid module, feature extraction module, K-means and k-nearest neighbor clustering algorithms and bring in the neighborhood module to build the CBIR system. Concept of neighborhood color analysis module which also recognizes the side of every grids of image is first contributed in this paper. The results show the CBIR systems performs well in the training and it also indicates there contains many interested issue to be optimized in the query stage of image retrieval.

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Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i1.pp486-495 ◽

2017 ◽

Vol 7 (1) ◽

pp. 486

Author(s):

Made Sudarma ◽

I Gede Harsemadi

Keyword(s):

Feature Extraction ◽

Processing Time ◽

Nearest Neighbor ◽

Extraction Process ◽

Performance Comparison ◽

K Nearest Neighbor ◽

K Value ◽

Id3 Algorithm ◽

Analysis System ◽

Music Information

Each of music which has been created, has its own mood which is emitted, therefore, there has been many researches in Music Information Retrieval (MIR) field that has been done for recognition of mood to music. This research produced software to classify music to the mood by using K-Nearest Neighbor and ID3 algorithm. In this research accuracy performance comparison and measurement of average classification time is carried out which is obtained based on the value produced from music feature extraction process. For music feature extraction process it uses 9 types of spectral analysis, consists of 400 practicing data and 400 testing data. The system produced outcome as classification label of mood type those are contentment, exuberance, depression and anxious. Classification by using algorithm of KNN is good enough that is 86.55% at k value = 3 and average processing time is 0.01021. Whereas by using ID3 it results accuracy of 59.33% and average of processing time is 0.05091 second.

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A High Performace of Local Binary Pattern on Classify Javanese Character Classification

Scientific Journal of Informatics ◽

10.15294/sji.v5i1.14017 ◽

2018 ◽

Vol 5 (1) ◽

pp. 8 ◽

Cited By ~ 1

Author(s):

Ajib Susanto ◽

Daurat Sinaga ◽

Christy Atika Sari ◽

Eko Hari Rachmawanto ◽

De Rosal Ignatius Moses Setiadi

Keyword(s):

Feature Extraction ◽

Image Classification ◽

Local Binary Pattern ◽

Nearest Neighbor ◽

Classification Algorithm ◽

K Nearest Neighbor ◽

Characteristic Extraction ◽

Research Objects ◽

Character Classification

The classification of Javanese character images is done with the aim of recognizing each character. The selected classification algorithm is K-Nearest Neighbor (KNN) at K = 1, 3, 5, 7, and 9. To improve KNN performance in Javanese character written by the author, and to prove that feature extraction is needed in the process image classification of Javanese character. In this study selected Local Binary Patter (LBP) as a feature extraction because there are research objects with a certain level of slope. The LBP parameters are used between [16 16], [32 32], [64 64], [128 128], and [256 256]. Experiments were performed on 80 training drawings and 40 test images. KNN values after combination with LBP characteristic extraction were 82.5% at K = 3 and LBP parameters [64 64].

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Pengenalan Wajah Menggunakan Metode Linear Discriminant Analysis dan k Nearest Neighbor

Jurnal ULTIMATICS ◽

10.31937/ti.v9i1.557 ◽

2017 ◽

Vol 9 (1) ◽

pp. 1-9

Author(s):

Fandiansyah Fandiansyah ◽

Jayanti Yusmah Sari ◽

Ika Putri Ningrum

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Nearest Neighbor ◽

Experimental Result ◽

K Nearest Neighbor ◽

Linear Discriminant ◽

Nearest Neighbor Classifier ◽

Neighbor Classifier

Face recognition is one of the biometric system that mostly used for individual recognition in the absent machine or access control. This is because the face is the most visible part of human anatomy and serves as the first distinguishing factor of a human being. Feature extraction and classification are the key to face recognition, as they are to any pattern classification task. In this paper, we describe a face recognition method based on Linear Discriminant Analysis (LDA) and k-Nearest Neighbor classifier. LDA used for feature extraction, which directly extracts the proper features from image matrices with the objective of maximizing between-class variations and minimizing within-class variations. The features of a testing image will be compared to the features of database image using K-Nearest Neighbor classifier. The experiments in this paper are performed by using using 66 face images of 22 different people. The experimental result shows that the recognition accuracy is up to 98.33%. Index Terms—face recognition, k nearest neighbor, linear discriminant analysis.

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

10.21203/rs.3.rs-82008/v1 ◽

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.

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COMPUTER-BASED IDENTIFICATION OF NORMAL AND ALCOHOLIC EEG SIGNALS USING WAVELET PACKETS AND ENERGY MEASURES

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519413500334 ◽

2013 ◽

Vol 13 (03) ◽

pp. 1350033 ◽

Cited By ~ 30

Author(s):

OLIVER FAUST ◽

WENWEI YU ◽

NAHRIZUL ADIB KADRI

Keyword(s):

Feature Extraction ◽

Nearest Neighbor ◽

Probabilistic Neural Network ◽

Gaussian Mixture ◽

Test Method ◽

Identification System ◽

Classification Algorithms ◽

K Nearest Neighbor ◽

Eeg Signals ◽

Computer Based

This paper describes a computer-based identification system of normal and alcoholic Electroencephalography (EEG) signals. The identification system was constructed from feature extraction and classification algorithms. The feature extraction was based on wavelet packet decomposition (WPD) and energy measures. Feature fitness was established through the statistical t-test method. The extracted features were used as training and test data for a competitive 10-fold cross-validated analysis of six classification algorithms. This analysis showed that, with an accuracy of 95.8%, the k-nearest neighbor (k-NN) algorithm outperforms naïve Bayes classification (NBC), fuzzy Sugeno classifier (FSC), probabilistic neural network (PNN), Gaussian mixture model (GMM), and decision tree (DT). The 10-fold stratified cross-validation instilled reliability in the result, therefore we are confident when we state that EEG signals can be used to automate both diagnosis and treatment monitoring of alcoholic patients. Such an automatization can lead to cost reduction by relieving medical experts from routine and administrative tasks.

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Discrimination of Chinese Liquors Based on Electronic Nose and Fuzzy Discriminant Principal Component Analysis

Foods ◽

10.3390/foods8010038 ◽

2019 ◽

Vol 8 (1) ◽

pp. 38 ◽

Cited By ~ 2

Author(s):

Xiaohong Wu ◽

Jin Zhu ◽

Bin Wu ◽

Chao Zhao ◽

Jun Sun ◽

...

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Electronic Nose ◽

Nearest Neighbor ◽

Principal Component ◽

Component Analysis ◽

K Nearest Neighbor ◽

Knn Classifier ◽

Extraction Algorithm ◽

Leave One Out

The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nose system can quickly detect different types of Chinese liquors according to their aromas. In this study, an e-nose system was designed to identify six types of Chinese liquors, and a novel feature extraction algorithm, called fuzzy discriminant principal component analysis (FDPCA), was developed for feature extraction from e-nose signals by combining discriminant principal component analysis (DPCA) and fuzzy set theory. In addition, principal component analysis (PCA), DPCA, K-nearest neighbor (KNN) classifier, leave-one-out (LOO) strategy and k-fold cross-validation (k = 5, 10, 20, 25) were employed in the e-nose system. The maximum classification accuracy of feature extraction for Chinese liquors was 98.378% using FDPCA, showing this algorithm to be extremely effective. The experimental results indicate that an e-nose system coupled with FDPCA is a feasible method for classifying Chinese liquors.

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