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A Novel learning vector quantization Inference Classifier 1

9th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Maisen, Chakkraphop Auephanwiriyakul, Sansanee Theera-Umpon, Nipon Chiang Mai Univ Fac Engn Dept Comp Engn Chiang Mai Thailand Chiang Mai Univ Grad Sch Chiang Mai Thailand Chiang Mai Univ Biomed Engn Ctr Chiang Mai Thailand Chiang Mai Univ Fac Engn Dept Elect Engn Chiang Mai Thailand

ISBN: (数字)9783319544724

ISBN: (纸本)9783319544724

One of the popular tools in pattern recognition is a neuro-fuzzy system. Most of the neuro-fuzzy systems are based on a multi-layer perceptrons. In this paper, we incorporate learning vector quantization in a neuro-fuzzy system. The prototype update equation is based on the learning vector quantization while the gradient descent technique is used in the weight update equation. Since weights contain informative information, they are exploited to select a good feature set. There are 8 data sets used in the experiment, i.e., Iris Plants, Wisconsin Breast Cancer (WBC), Pima Indians Diabetes, Wine, Ionosphere, Colon Tumor, Diffuse Large B-Cell Lymphoma (DLBCL), and Glioma Tumor (GLI_85). The results show that our algorithm provides good classification rates on all data sets. It is able to select a good feature set with a small number of features. We compare our results indirectly with the existing algorithms as well. The comparison result shows that our algorithm performs better than those existing ones.

关键词： Neuro-fuzzy classifier learning vector quantization Prototype-based rule Rule extraction Feature selection

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Anomaly based Detection Analysis for Intrusion Detection System using Big Data Technique with learning vector quantization (LVQ) and Principal Component Analysis (PCA) 2018

Anomaly based Detection Analysis for Intrusion Detection Sys...

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International Conference on Artificial Intelligence and Virtual Reality (AIVR) / International Conference on Big Data and Machine learning (BDML)

作者： Salman, Muhammad Husna, Diyanatul Apriliani, Stella Gabriella Pinem, Josua Geovani Univ Indonesia Dept Elect Engn Fac Engn Depok Indonesia

ISBN: (纸本)9781450366410

Data security has become a very serious parf of any organizational information system. More and more threats across the Internet has evolved and capable to deceive firewall as well as antivirus software. In addition, the number of attacks become larger and become more dificult to be processed by the firewall or antivirus software. To improve the security of the system is usually done by adding Intrusion Detection System(IDS), which divided into anomaly-based detection and signature-based detection. In this research to process a huge amount of data, Big Data technique is used. Anomaly-based detection is proposed using learning vector quantization Algorithm to detect the attacks. learning vector quantization is a neural network technique that learn the input itself and then give the appropriate output according to the input. Modifications were made to improve test accuracy by varying the test parameters that present in LVQ. Varying the learning rate, epoch and k-fold cross validation resulted in a more efficient output. The output is obtained by calculating the value of information retrieval from the confusion matrix table from each attack classes. Principal Component Analysis technique is used along with learning vector quantization to improve system performance by reducing the data dimensionality. By using 18-Principal Component, dataset successfully reduced by 47.3%, with the best Recognition Rate of 96.52% and time efficiency improvement up to 43.16%.

关键词： Big Data IDS learning vector quantization Network Security Principal Component Analysis (key words)

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Biometric Recognition based on Palm Vein Image Using learning vector quantization 5

Biometric Recognition based on Palm Vein Image Using Learnin...

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5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME)

作者： Setiawan, Herry Yuniarno, Eko Mulyanto Polytech South Acch Dept Comp Sci Tapaktuan Indonesia Inst Technol Sepuluh Nopember Dept Elect Engn Surabaya Indonesia

ISBN: (纸本)9781538634554

Every human being has its own uniqueness among humans that other good physical form or characteristic trait. Biometrics is the science that can recognize a part of an individual. Therefore, biometric identification is one of the ways that is done to identify the identity of the person. Palm vein biometric is one who lately attracted many researchers and industry because it has several advantages over other physical characteristics such as fingerprint, iris and face. Palm vein has internal features making it difficult undermined, modified and simulated with fake palms. In this study, design and implementation will do a recognition system through the venous vessels. Systems that were built capable of taking images of the Palm veins, detect the presence of venous vessels are then able to tell based on the database. This is done after the process of extraction, processing and manufacturing characteristics against the image of Palm vein vessels. The method used is the phase and learning vector quantization symmetri. Feature extraction using phase symmetri. phase symmetri applied by calculating the amplitude and phase of the signal frequency of the image of the vein which represents detailed information detailed variant of an image of the vein. While learning vector quantization is used for grouping the exclusive result of extraction. This research resulted in the classification with accuracy reaching 94%

关键词： Biometric pattern recognition palm vein learning vector quantization phase symmetri

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Automatic Essay Grading System with Latent Semantic Analysis and learning vector quantization 17

Automatic Essay Grading System with Latent Semantic Analysis...

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3rd International Conference on Communication and Information Processing (ICCIP)

作者： Ratna, Anak Agung Putri Raharjo, Budi Selamet Purnamasari, Prima Dewi Sanjaya, Randy Univ Indonesia Fac Engn Dept Elect Engn Kampus Baru UI Depok 16424 Indonesia

ISBN: (纸本)9781450353656

Automatic essay grading system called SIMPLE-O (Sistem Penilai Esai Otomatis) that developed by Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia was built using PHP. This system was developed for helping lecturer assessing the examination, especially with essay form. Currently, the SIMPLE-O is still developed using C programming language for implementing more methods in development that can only be done in that language to improve the performance of the system. To increase the accuracy, learning vector quantization (LVQ) algorithm is implemented in the development due to its ability for supervised classification. The number of data samples in LVQ training phase are affecting the essay scoring performance, less data used will lead to decrease in the result accuracy of the validation phase. Moreover, singular value that generated by Frobenius norm and vector angle pre-processing will also affect the scoring accuracy. But, the number of words-per-column when creating the LSA matrix did not have any significant effect. At the end, SIMPLE-O with LVQ has an average accuracy of 53.57%, 41.66% higher than the system that did not use LVQ. This accuracy performance was still low due to the missing of the words similarity feature. In the previous version of SIMPLE-O, this feature can improve the performance of the essay grading system significantly.

关键词： Essay grading latent semantic analysis learning vector quantization SIMPLE-O singular value decomposition

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Application of Unsupervised K Nearest Neighbor (UNN) and learning vector quantization (LVQ) Methods in Predicting Rupiah to Dollar 4

Application of Unsupervised K Nearest Neighbor (UNN) and Lea...

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4th International Conference on Cyber and IT Service Management

作者： Haerani, Elin Apriyanti, Liza Wardhani, Luh Kesuma UIN Sultan Syarif Kasim Informat Fac Sci & Technol Kota Pekanbaru Riau Indonesia UIN Syarif Hidayatullah Informat Fac Sci & Technol South Tangerang Indonesia

ISBN: (纸本)9781467384414

One of the factors in a country's economy is the exchange value of the currency towards another currency. The exchange value of Rupiah towards Dollar (USA) can quickly change depending on the environmental conditions and has a huge impact for the Indonesian Government. In this research, learning vector quantization (LVQ) and Unsupervised K Nearest Network (UNN) was implemented in predicting the currency value towards dollar. The UNN method was used to predict the selling value of the currency, the LVQ method was used to predict the buying value of the currency. The input data that is used is the selling, buying and interest data times series of the currency from the central back of the United States. From the research result and discussions that was made, UNN can achieve the lowest MAPE, which is 1,544% with the amount of data as much as 25 and the LVQ algorithm can accurately achieve a forecast with the amount of data as much as 25 with the learning rate of 0,075. The amount of trained data and the many patterns that exist in one LVQ class method can affect the result of the study and the result of the system.

关键词： Dollar Foreign Exchange Rates learning vector quantization LVQ Rupiah Unsupervised K Nearest Network UNN

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Weighted learning vector quantization to Cost-Sensitive learning

Weighted Learning Vector Quantization to Cost-Sensitive Lear...

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20th International Conference on Artificial Neural Networks

作者： Chen, Ning Ribeiro, Bernardete Vieira, Armando Duarte, Joao Neves, Joao Inst Politecn Porto GECAD Inst Super Engn Porto Oporto Portugal Univ Coimbra CISUC Dept Informat Engn Coimbra Portugal Univ Tecn Lisboa ISEG Sch Econo Lisbon Portugal

ISBN: (纸本)9783642158247

The importance of cost-sensitive learning becomes crucial when the costs of misclassifications are quite different. Many evidences have demonstrated that a cost-sensitive predictive model is more desirable in practical applications than a traditional one without taking the cost into consideration. In this paper, we propose two approaches which incorporate the cost matrix into original learning vector quantization by means of instance weighting. Empirical results show that the proposed algorithms are effective on both binary-class data and multi-class data.

关键词： classification neural network cost-sensitive learning learning vector quantization

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Loss-of-load probability calculation using learning vector quantization

Loss-of-load probability calculation using learning vector q...

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International Conference on Power System Technology (POWERCON 2000)

作者： Luo, XC Singh, C Zhao, Q Texas A&M Univ Dept Elect Engn College Stn TX 77843 USA

ISBN: (纸本)0780363388

This paper proposes a new method employing learning vector quantization (LVQ) and Monte Carlo simulation to calculate the loss-of-load probability (LOLP) of power systems. LVQ is a type of classification method whose goal is to use data samples to position the codebook vector in such a way that the nearest neighbor classification method will result in the maximum classification accuracy. The proposed method greatly reduces the computing burden of the loss-of-load probability calculation compared to Monte Carlo simulation only. A case study of the IEEE RTS system is presented demonstrating the efficiency of this approach.

关键词： learning vector quantization optimal power flow loss-of-load probability reliability evaluation

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A combined method of optimized learning vector quantization and neuro-fuzzy techniques for predicting unified Parkinson's disease rating scale using vocal features

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METHODSX 2024年 12卷 102553页

作者： Zogaan, Waleed Abdu Nilashi, Mehrbakhsh Ahmadi, Hossein Abumalloh, Rabab Ali Alrizq, Mesfer Abosaq, Hamad Alghamdi, Abdullah Jazan Univ Fac Comp Sci & Informat Technol Dept Comp Sci Jazan 45142 Saudi Arabia UCSI Univ UCSI Grad Business Sch Kuala Lumpur 56000 Malaysia Univ Sains Malaysia Ctr Global Sustainabil Studies CGSS George Town 11800 Penang Malaysia Univ Plymouth Fac Hlth Ctr Hlth Technol Plymouth PL4 8AA England Univ Plymouth Fac Hlth Plymouth PL4 8AA England Qatar Univ Dept Comp Sci & Engn Doha 2713 Qatar Najran Univ Coll Comp Sci & Informat Syst Informat Syst Dept Najran Saudi Arabia Najran Univ Sci & Engn Res Ctr SERC Najran Saudi Arabia Najran Univ Coll Comp Sci & Informat Syst Comp Sci Dept Najran Saudi Arabia

Parkinson's Disease (PD) is a common disorder of the central nervous system. The Unified Parkinson's Disease Rating Scale or UPDRS is commonly used to track PD symptom progression because it displays the presence and severity of symptoms. To model the relationship between speech signal properties and UPDRS scores, this study develops a new method using Neuro-Fuzzy (ANFIS) and Optimized learning Rate learning vector quantization (OLVQ1). ANFIS is developed for different Membership Functions (MFs). The method is evaluated using Parkinson's telemonitoring dataset which includes a total of 5875 voice recordings from 42 individuals in the early stages of PD which comprises 28 men and 14 women. The dataset is comprised of 16 vocal features and Motor-UPDRS, and Total-UPDRS. The method is compared with other learning techniques. The results show that OLVQ1 combined with the ANFIS has provided the best results in predicting Motor-UPDRS and Total-UPDRS. The lowest Root Mean Square Error (RMSE) values (UPDRS (Total)=0.5732;UPDRS (Motor)=0.5645) and highest R-squared values (UPDRS (Total)=0.9876;UPDRS (Motor)=0.9911) are obtained by this method. The results are discussed and directions for future studies are presented. i. ANFIS and OLVQ1 are combined to predict UPDRS. ii. OLVQ1 is used for PD data segmentation. iii. ANFIS is developed for different MFs to predict Motor-UPDRS and Total-UPDRS.

关键词： Parkinson's disease Neuro-fuzzy Optimized learning rate Motor-UPDRS Total-UPDRS learning vector quantization

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Implementation of EOG Mouse Using learning vector quantization and EOG-feature Based Methods

Implementation of EOG Mouse Using Learning Vector Quantizati...

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IEEE Conference on Systems, Process and Control (ICSPC)

作者： Zhang, Peng Ito, Momoyo Ito, Shin-ichi Fukumi, Minoru Univ Tokushima Sys Innovat Engn Tokushima 770 Japan Univ Tokushima Inst Sci & Technol Tokushima 770 Japan

ISBN: (纸本)9781479922086

It is difficult for patients with severe physical disabilities to communicate with others, such as amyotrophic lateral sclerosis and serious paraplegia. Owing to the illness in which they lost their limb motor function and language function, they cannot move even their muscles except eye. In order to provide an efficient means of communication for those patients, in this paper we proposed a system that uses EOG-feature based methods and learning vector quantization algorithm to recognize eye motions. According to the recognition results, we use API (application programming interface) to control cursor movements. The recognition part consists of four steps. First, we measure EOG signals by every 1.8 seconds. Next, we make a judge whether eye motion subsists in the 1.8 seconds EOG data, if any, we extract the data of each motion from the 1.8 seconds EOG data. After that we use Fast Fourier Transform to obtain the frequency features of the extracted motion. Finally we use learning vector quantization network and characteristics of EOG features at each motion to recognize eye motions. The LVQ network is trained beforehand. In this paper we recognized motions of rolling eye upward, rolling downward, rolling left, rolling right, blink and diagonal eye motions which contain rolling up-left, rolling up-right, rolling down-left, rolling downright (the angle of the diagonal motion is 45 degrees) and blink string of three times motion. 8 directions motions correspond to 8 directions cursor movement in this system. We regard blink motion as invalid signal and define blink string motions as double click action. Using this system we have obtained a high recognition accuracy of eye motions (The average correct detection rate on each subject was 97.8%, 97.6% and 92.7%). This EOG Mouse interface would be used as a means of communication to help those patients as ALS.

关键词： Electrooculography learning vector quantization eye motions recognition EOG Mouse

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Feature Selection with Particle Swarm Optimization for Human Activity Recognition Using learning vector quantization 21

Feature Selection with Particle Swarm Optimization for Human...

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6th International Conference on Sustainable Information Engineering and Technology (SIET) - Innovation for Smart Societies

作者： Bachtiar, Fitra Abdurrahman Arifien, Zainal Putra, Adzanil Rachmadi Akbar, Pasca Imanuddin Univ Brawijaya Fac Comp Sci Malang Indonesia

ISBN: (纸本)9781450384070

As a social being, humans must do a variety of activities ranging from waking up in the morning to going back to sleep at night. Of the many activities, this research wants to find out how many activities humans do, for example how many times people walk, lie down, and so on in a day. To find out the number of these activities, this study uses learning vector quantization (LVQ) method for recognizing human activities. This study uses 561 features from 1000 data to be used for the training process and 500 data to be used in the testing process. Data with a large number of features can affect the length of the calculation process. Because LVQ uses features as input data, we need a method that can reduce the number of features. One method that can be used is the Particle Swarm Optimization (PSO) method. The training process is carried out using the PSO selection feature with the parameters of the number of particles 30, maximum iterations 30, the value of omega 0.3, Phi p of 0.3 and Phi g of 0.8. From the training process, gBest and LVQ parameters are obtained with a value of alpha 0.1, a reduction of alpha (dec alpha) 0.5, minimum alpha 0,0000001 and a maximum of epoch or iteration 5 with a final accuracy of 17%.

关键词： Particle Swarm Optimization learning vector quantization Feature Selection Human Activity Recognition

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