Implementation of Learning Vector Quantization (LVQ) Algorithm for Durian Fruit Classification Using Gray Level Co-occurrence Matrix (GLCM) Parameters

This paper proposes a framework for developing a broad variety of soft clustering and learning vector quantization (LVQ) algorithms based on gradient descent minimization of a reformulation function. According to the proposed axiomatic approach to learning vector quantization, the development of specific algorithms reduces to the selection of a generator function. A linear generator function leads to the fuzzy c-means (FCM) and fuzzy LVQ (FLVQ) algorithms while an exponential generator function leads to entropy constrained fuzzy clustering (ECFC) and entropy constrained LVQ (ECLVQ) algorithms. The reformulation of clustering and LVQ algorithms is also extended to supervised learning models through an axiomatic approach proposed for reformulating radial basis function (RBF) neural networks. This approach results in a broad variety of admissible RBF models, while the form of the radial basis functions is determined by a generator function. This paper shows that gradient descent learning makes reformulated RBF neural networks an attractive alternative to conventional feed-forward neural networks.

From Aggregation Operators to Soft Learning Vector Quantization and Clustering Algorithms

Social media as a communication medium to connect everyone has become widely accepted by the community along with the development of information and technology. Data from social media that contains opinions from the public can be processed into information that is more important in value, one of which is by using sentiment analysis. In this Final Project research, sentiment analysis was carried out using the Learning Vector Quantization (LVQ) algorithm. The first step is retrieving Tweet data using the Twitter API. After that, the data preprocessing was done to process the data and extract the features contained in it. After that, the feature list is converted into vector form using TF-IDF weighting. The accuracy performance of the method is obtained by testing the learning rate, epoch, and amount of data used. From the results of testing the performance of the Learning Vector Quantization (LVQ) algorithm, it is known that the higher the learning rate, the smaller the accuracy obtained, then the higher the number of epochs, the greater the accuracy. The average accuracy achieved in this study was 73.15%, 66.42%, and 69.58% for each data.

The learning vector quantization (LVQ) algorithm is one of the popular quantization algorithms. A new version of LVQ is developed. The new version requires less time for the training phase and has better performance on data which include each other.

Rainfall is a very important factor in agriculture and development planning. This study aims to predict rainfall and rainfall properties in Medan using a multilayer neural network with Backpropagation and Learning Vector Quantization (LVQ) algorithms. Rainfall data used for training is rainfall data for the last 30 years. The parameters used in this study consisted of two inputs namely the month and the volume of rainfall. The training process produces the best architecture with 3 layer three for Backpropagation and two layers for Learning Vector Quantization with learning rate 0.5. From the prediction results obtained Backpropagation algorithm more accurate in predicting rainfall data last 30 years compared with LVQ with an average difference of 21.99%. Backpropagation and LVQ algorithms have better accuracy in the dry season, with accuracy for Backpropagation algorithm between 75 - 99% and LVQ algorithm of 60 - 82%. For both algorithms, the influence of El-Nino and La-Nina phenomena is not so significant.

We propose a novel kernel prototype-based learning algorithm, called kernel generalized learning vector quantization (KGLYQ) algorithm, which can significantly improve the classification performance of the original generalized learning vector quantization algorithm in complex pattern classification tasks. In addition, the KGLVQ can also serve as a good general kernel learning framework for further investigation.

Identification of driving activities is important to find out abnormal driving conditions such as accidents. In this work, identifying driving activities is carried out using the LVQ (Learning Vector Quantization) algorithm. This algorithm creates a prototype that is easily interpreted for experts in each application domain. The dataset for each driving activity is obtained from the accelerometer sensor and the android smart gyroscope. The exponential smoothing method is used in the sensor dataset to improve the accuracy of classification results. The best accuracy is obtained from the classification of the gyroscope sensor dataset after smoothing with an accuracy of 90.429%.

— Neural networks are frequently used as a classifier for tasks in many classifications. However there are disadvantages in terms of amount of training data required, and length of training time. This paper, develop an intelligent diagnosis system for zinc oxide (ZnO) surge arrester fault classification. First the features were extracted from 600 ZnO surge arrester thermal images and leakage currents. Then these features were presented to several neural network architectures to investigate the most suitable network model for classifying the ZnO surge arrester fault condition effectively. Three classification models were used namely feed forward back propagation (FFBP), radial basis function (RBF) and learning vector quantization (LVQ) algorithm. The performance of the networks was compared based on resulted of misclassify and correct rate. The method was evaluated using 24 testing datasets. Comparison results showed that LVQ was the best training algorithm for the ZnO surge arrester fault classification compared to the others system. Also the LVQ is faster than FFBP and RBF.

Clustering a power system is very useful for the purpose of voltage stability control. However, the methods have developed usually have computational inefficiency. This paper presents a new cluster bus technique using Kohonen neural network for the purpose of forming bus clusters in power systems from the voltage stability viewpoint. This cluster formation will simplify voltage control in power system. With this proposed Kohonen algorithm, a large bus system will be partitioned into a small bus groups that have a coherence V, �, P and Q. The maximum number of area clusters will be formed need for voltage stability needed. The proposed technique was tested on IEEE 39 bus system by considering two contingency namely load increased and line outage by using voltage collapse analysis. This formation will be compared with the Learning Vector Quantization (LVQ) algorithm. The results showed the proposed technique produces four clusters on contingency load load increased and three clusters online outage contingency on IEEE 39 bus system as shown by the LVQ.

Decentralized estimation is an essential problem for a number of data fusion applications. In this paper we propose a variation of the Learning Vector Quantization (LVQ) algorithm, the Distortion Sensitive LVQ (DSLVQ), to be used for quantizer design in decentralized estimation. Experimental results suggest that DSLVQ results in high-quality quantizers and that it allows easy adjustment of the complexity of the resulting quantizers to computational constraints of decentralized sensors. In addition, DSLVQ approach shows significant improvements over the popular LVQ2 algorithm as well as the previously proposed Regression Tree approach for decentralized estimation.

On the use of the weighted fuzzy c-means in fuzzy modeling

An advanced neuro-classification of new and used bills using the spectral patterns is proposed. An acoustic spectral pattern is obtained from the output of the two-stage adaptive digital filters (ADFs) for time-series acoustic data. The acoustic spectral patterns are fed to a competitive neural network, and classified into some categories which show worn-out degrees of the bill. The proposed method is based on extension of an ADF, an individual adaptation (IA) algorithm, and a learning vector quantization (LVQ) algorithm. The experimental results show that the proposed method is useful to classify new and used bills.

Defining decision region borders properly is a major task of classification algorithms. In this paper, the border feature detection and adaptation (BFDA) algorithm is introduced for this purpose. The BFDA is a novel classification scheme, especially useful for the classification of remote sensing images. The method exploits the powerful discrimination capability of the 1-nearest neighborhood (1-NN) method with the border feature vectors. The first part of the algorithm consists of generating border feature vectors using class centers and misclassified training vectors. With this approach, a manageable number of border feature vectors are obtained. The second part of the algorithm involves the adaptation of the border feature vectors with a technique similar to the learning vector quantization (LVQ) algorithm. The performance of the BFDA was compared with other classification algorithms including support vector machines (SVMs) and several statistical classification techniques.

LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer’s disease

The face is one of the media to identify someone, a human face has a very high level of variability. Many methods have been introduced by researchers and scientists in recognizing one's face, one of the methods introduced is the Feature Extraction of Gray Level Co-Occurrence Matrix (GLCM) and Learning Vector Quantization (LVQ). GLCM feature extraction is used for data extraction/learning process whereas a data analysis process (face recognition, cropping and storing data) the LVQ method is used for the data training process where the data that has been processed in GLCM feature extraction which still has large dimensions are processed to be smaller dimensions. So this test uses data of 190 photos and gets a match of 90%, the authors conclude that the GLCM feature extraction and LVQ method can very well recognize faces contained in the database.