Learning Vector Quantization Classifier Research Articles

Data sampling approach using heuristic Learning Vector Quantization (LVQ) classifier for software defect prediction

On the basis of quality estimate, early prediction and identification of software flaws is crucial in the software area. Prediction of Software Defects SDP is defined as the process of exposing software to flaws through the use of prediction models and defect datasets. This study recommended a method for dealing with the class imbalance problem based on Improved Random Synthetic Minority Oversampling Technique (SMOTE), followed by Linear Pearson Correlation Technique to perform feature selection to predict software failure. On the basis of the SMOTE data sampling approach, a strategy for software defect prediction is given in this paper. To address the class imbalance, the defect datasets were initially processed using the Improved Random-SMOTE Oversampling technique. Then, using the Linear Pearson Correlation approach, the features were chosen, and using the k-fold cross validation process, the samples were split into training and testing datasets. Finally, Heuristic Learning Vector Quantization is used to classify data in order to predict software problems. Based on measures like sensitivity, specificity, FPR, and accuracy rate for two separate datasets, the performance of the proposed strategy is contrasted with the approaches to classification that presently exist.

Implementation of Principal Component Analysis and Learning Vector Quantization for Classification of Food Nutrition Status

Balanced nutrition is very good in the process of child development. During the COVID-19 pandemic, consuming a balanced nutritious diet can keep a child's immune system from transmitting the virus. In determining the nutritional content of children's food during the pandemic, a classification of the nutritional content of children's food is carried out by applying the principal component analysis (PCA) dimension reduction method and the learning vector quantization (LVQ) classification method. The data used in this study amounted to 1168 data with 25 indicators of food nutrients. From the tests that have been carried out, the combination of the PCA-LVQ method produces an average accuracy of 58% with the highest accuracy of 60%. In addition, this study also compares the performance of the PCA dimension reduction method, independent component analysis (ICA) and factor analysis (FA) on the LVQ classification process. The final result of testing the three methods is that the FA method takes the fastest time, which is 4.10434 seconds and the PCA method produces the highest accuracy, which is 58.2%

Software Defect Prediction and Software Quality Assessment Using Dlr-Lvq and Fuzzy Rules

Recently, Software development has been considerably grown. Fault in the software causes fault and interrupts the output. Characteristics like these make it much challenging to avert software flaws. Spontaneously forecasting the amount of flaws within the software modules is essential and also can assist developers to proficiently allot restricted resources. Recently, numerous Software Defect Prediction (SDP) techniques are developed. But, the accuracy and time consuming challenges still remain to be solved. Also, a few top-notch techniques don't properly classify the software whereas it is a needed metric to ensure quality standards. This work proffers a novel Decaying Learning Rate – Learning vector Quantization (DLR-LVQ) classifier to forecast the software defect. The proposed methods consist of the following steps: redundant data removal, feature extraction (FE), feature oversampling, data normalization, defect prediction (DP), and quality prediction. The proposed DLR-LVQ’s attained outcome is assessed with the existent methodologies. The outcomes exhibit that the methodology proposed attains efficient classification outcomes are examined. Keywords: Software Defect Prediction (SDP), Non defective software quality prediction, BM-SMOTE, Decaying Learning Rate, Learning Vector Quantization, Fuzzy rules, HDFS and Map Reduce.

Comparison of Classifiers for the Prediction of Liver Cancer Disease

The detection of liver cancer in its early stages is very difficult. The proposed system collects microscopic images as input from the patients and preprocesses them to extract features. Once the feature extraction stage is completed the classification of the image need to be done on them. The proposed system uses the probabilistic Neural Networks (PNN), K-nearest neighbor (KNN), and Learning Vector Quantization (LVQ) classifiers to classify the images into their respective classes. The classifier system uses the normal approach of classification i.e., a normally two stages one is training and then testing. Each of these classifiers goes through both these stages. Firstly, the training stage involves the system learning on the images and their respective category which is already known from the expert advice. In this way a series of images are given in the form of an input with their actual category. The classifier learns from this and then in the testing phase a new image is given for classification to the system. The system uses the prior knowledge which it has learnt during the training phase to predict the category for the image. The accuracy of each classifier is being calculated to apply the classifier with more accuracy for the prediction of the disease.

Descending stairs and floors classification as control reference in autonomous smart wheelchair

Disability is a disruption or limitation of a person’s body functions in carrying out daily activities. A person with physical disabilities needs an assistive device such as a wheelchair. The latest wheelchair development is the smart wheelchair. Smart wheelchairs require a control system to detect obstacles quickly. This aims to provide safety, especially for users. One of the obstacles that are quite dangerous is descending stairs. Therefore the researchers propose a descending stairs detection system for smart wheelchairs. The proposed method in this study is the gray level co-occurrence matrix (GLCM) as the feature extraction algorithm, learning vector quantization (LVQ) as the classification algorithm, and sequential forward selection (SFS) for feature selection. Based on the simulation result, the SFS feature selection gets two selected GLCM features. The best accuracy is 94.5% with the selected features, namely contrast and dissimilarity. This result has an increase in accuracy when compared to using the GLCM 6 feature method with the LVQ classification that does not use feature selection, where the method gets 92.5% accuracy in off-time testing. Accuracy decreased to 78.21% when detecting floors and 89.06% when detecting descending stairs in real-time system testing.

PENGARUH TRANSFORMASI DATA PADA METODE LEARNING VECTOR QUANTIZATION TERHADAP AKURASI KLASIFIKASI DIAGNOSIS PENYAKIT JANTUNG

Learning Vector Quantization (LVQ) is a type of Artificial Neural Network with a supervised learning process based on competitive learning. Despite the absence of assumptions in LVQ is an advantage, it can be a problem when the predictor variables have big different ranges.This problems can be overcome by equalizing the range of all variables by data transformation so that all variables have relatively same effect. Heart Disease UCI dataset which used in this study is transformed by several transformation methods, such as minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax. The result show that the six transformed data can provide better LVQ classification accuracy than the raw data which has 75.99% for training performance accuracy. LVQ classification accuracy with data transformation of minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax are 89.16%, 88.22%, 89.7%, 90.1%, 88.17% and 92.18%. Based on the One-way ANOVA test and DMRT post hoc test known that there are significant differences between the results of the classification with data transformations and raw data in 0,05 significant level of α. It is also known that the best data transformation methods are softmax for training and sigmoid for testing. Keywords: heart disease, neural network, learning vector quantization, classification, data transformation

Pattern Recognition of Facial Electromyography (FEMG) Signal for Aceh Language Speech using Naïve Bayes and Learning Vector Quantization (LVQ)

Facial electromyography is myoelectric signals that formed by human facial muscles. This signal can be acquired by attaching an electrode to the facial muscle that has been connected with an electromyography sensor. When human say certain words, the articulation muscles contract and facial electromyography signals appear in the muscles. This study aims to recognize patterns in facial electromyography signals by classifying signals using naïve bayes and learning vector quantization classifier. Feature extraction used one-dimensional discrete wavelet transforms. Wavelet transform type used wavelet daubechies2 level 5. The transformation produces a level 5 approximation coefficient called a5 and five detail coefficients called d1, d2, d3, d4, and d5. The result of this study show that the average classification accuracy for ho neuk jak sentence using naïve bayes and LVQ classifier was 62.5% and 92.5% respectively. The average classification accuracy for ja’ word using naïve bayes and LVQ classifier was 70% and 92.5% respectively. The average classification accuracy for ja’ wo sentence using naïve bayes and LVQ classifier was 52.5% and 90% respectively. The average classification accuracy for pane word using naïve bayes and LVQ was 70% and 90% respectively. The average classification accuracy for soe word using naïve bayes and LVQ classifier is 85% and 95% respectively. Thus, this study shows that when humans say the words, facial electromyography signals that appear on facial muscles difference for each subject.

NIMG-59. EVALUATION OF GLCM FEATURES BASED CLASSIFIERS IN DIFFERENTIATING TUMOR GRADES ON MULTI-MODAL MR IMAGES

Abstract BACKGROUND Accurate classification of tumor grade is necessary for understanding tumor development critical in patient management. Radiomic features are gaining popularity in classifying the tumors with the application of various classifiers. We evaluate five classifiers using gray-level co-occurrence matrix (GLCM) features to classify low-grade gliomas (LGG) and high-grade gliomas (HGG). METHODS We included high resolution multi-modal MR images from pre-operative BraTS 2019 database. The database contains a total of 335 multi-modal MR images (259 HGG and 76 LGG) with manually-corrected segmentations of tumor compartments. Sixty four three-dimensional (3D) voxel-by-voxel GLCM feature images on T2-wighted (T2), fluid attenuated inversion recovery (FLAIR), and T1-weighted (T1) pre- and post-contrast images are computed. A total of 192 features within regions of enhanced tumor (ET), edema (ED), and non-enhanced tumor (NET) are obtained by taking averages of the GLCM features within each region. For classification, we evaluated k-nearest neighbor (kNN), learning vector quantization (LVQ), random forest classifier (RF), classification and regression trees (CART), and support vector machine (SVM) classifiers for differentiating LGG from HGG. The dataset is randomly split into ratio of 80 to 20 for training and validation. The models are trained using repeated 5-fold cross-validation. Best 10 models for each of the classifiers are selected based on accuracy by applying multiple random split. RESULTS The average accuracies of 10 best models selected for each of kNN, LVQ, RF, CART, and SVM classifiers are 0.88, 0.92, 1.00, 0.890, and 0.95 on training set, and 0.89, 0.88, 0.88, 0.89, and 0.90 on validation set. The performance of five classifiers on validation set is similar. The accuracy of SVM classifier is slightly higher on validation set even though the RF appears to be the best classifier on training set. CONCLUSION Voxel-by-voxel GLCM features help differentiate LGG and HGG with 0.89 of accuracy irrespective of the classifier.

Verifikasi Tanda Tangan Menggunakan Ekstraksi Fitur LBP dan Klasifikasi LVQ

Signature is one of the media used for verification and legalization of information, such as documents that are closely related to legality. In general, signature verification is done manually by direct comparing, this is certainly not effective, especially if doing a lot verification. Therefore, we need a computer system that can automatically verify a person's signature to save time in matching and reducing errors. This research was conducted using feature of Local Binary Pattern (LBP) method and Learning Vector Quantization (LVQ) classifier. Materials that used in this research are 600 signature images with a size of 500x500 pixels taken from 30 respondents where each respondent taken 15 original signatures and 5 fake signatures. The results of this research are that the signature identification process resulted in 93% and the verification process resulted in an accuracy of 63%, a sensitivity of 89%, and a specificity of 42%.

Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization.

Multipose face recognition system is one of the recent challenges faced by the researchers interested in security applications. Different researches have been introduced discussing the accuracy improvement of multipose face recognition through enhancing the face detector as Viola-Jones, Real Adaboost, and Cascade Object Detector while others concentrated on the recognition systems as support vector machine and deep convolution neural networks. In this paper, a combined adaptive deep learning vector quantization (CADLVQ) classifier is proposed. The proposed classifier has boosted the weakness of the adaptive deep learning vector quantization classifiers through using the majority voting algorithm with the speeded up robust feature extractor. Experimental results indicate that, the proposed classifier provided promising results in terms of sensitivity, specificity, precision, and accuracy compared to recent approaches in deep learning, statistical, and classical neural networks. Finally, the comparison is empirically performed using confusion matrix to ensure the reliability and robustness of the proposed system compared to the state-of art.

Online semi-supervised learning with learning vector quantization

Online semi-supervised learning (OSSL) is a learning paradigm simulating human learning, in which the data appear in a sequential manner with a mixture of both labeled and unlabeled samples. Despite the recent advances, there are still many unsolved problems in this area. In this paper, we propose a novel OSSL method based on learning vector quantization (LVQ). LVQ classifiers, which represent the data of each class by a set of prototypes, have found their usage in a wide range of pattern recognition problems and can be naturally adapted to the online scenario by updating the prototypes with stochastic gradient optimization. However, most of the existing LVQ algorithms were designed for supervised classification. To extract useful information from unlabeled data, we propose two simple and computationally efficient methods based on clustering assumption. To be specific, we use the maximum conditional likelihood criterion for updating prototypes when data sample is labeled, and the Gaussian mixture clustering criterion or neural gas clustering criterion for adjusting prototypes when data sample is unlabeled. These two criteria are utilized alternatively according to the availability of label information to make full use of both supervised and unsupervised data to boost the performance. By extensive experiments, we show that the proposed method exhibits higher accuracy compared with the baseline methods and graph-based methods and is much more efficient than graph-based methods in both training and test time.

Automatic Deep Vector Learning Model Applied for Oil-Well-Testing Feature Mining, Purification and Classification

Well-testing stage analysis is an important way for oilfield operation state decision-making and reservoir management. However, due to the variability and nonlinearity of the downhole data caused by the complex exploration activities and the differences of petroleum type and geological conditions, the classical methods are ineffective in feature extraction, learning network construction, and classifier optimization. In this work, we propose a new well-testing stage classification method based on a deep vector learning model (DVLM). The novelty of this study lies in the combination of multi-feature extraction, deep learning and feature vector mapping. The proposed method can overcome the problems of poor feature representation ability and poor classification model generalization ability in the existing machine learning methods, which mainly caused by the non-optimized training network structure and the unreasonable classifier design,. Firstly, the initial features are obtained by four classical methods. Then a five layers deep belief network embedded with the mutual information coefficient method is implemented for further feature extraction and purification. Finally, the optimized learning vector quantization classifier outputs the predicted tags. For model training and testing 572 field samples total of 4004 data streams are used. By considering the classification errors and accuracy metrics, the neurons number of deep learning network and the classifier are tuned, and an optimal and stable framework is obtained. Comparative experimental results with several classical integration models show that the proposed model achieves the highest classification accuracy of 98.065% as well as the least of features (nine). The results demonstrate that the proposed model has excellent performance in improving the classification accuracy and completing the feature compression. Moreover, the proposed model has very important practical significance for guiding the automatic analysis and processing oil and gas data.

A Genetic-LVQ neural networks approach for handwritten Arabic character recognition

Handwritten recognition systems are a dynamic field of research in areas of artificial intelligence. Many smart devices available in the market such as pen-based computers, tablets, mobiles with handwritten recognition technology need to rely on efficient handwritten recognition systems. In this paper we present a novel Arabic character handwritten recognition system based on a hybrid method consisting of a genetic algorithm and a Learning vector quantization (LVQ) neural network. Sixty different handwritten Arabic character datasets are used for training the neural network. Each character dataset contains 28 letters written twice with 15 distinct shaped alphabets, and each handwritten Arabic letter is represented by a binary matrix that is used as an input to a genetic algorithm for feature selection and dimension reduction to include only the most effective features to be fed to the LVQ classifier. The recognition process in the system involves several essential steps such as: handwritten letter acquisition, dataset preparation, feature selection, training, and recognition. Comparing our results to those acquired by the whole feature dataset without selection, and to the results using other classification algorithms confirms the effectiveness of our proposed handwritten recognition system with an accuracy of 95.4%, hence, showing a promising potential for improving future handwritten Arabic recognition devices in the market.

Feature weighting using particle swarm optimization for learning vector quantization classifier

This paper discusses and proposes a method of feature weighting in classification assignments on competitive learning artificial neural network LVQ. The weighting feature method is the search for the weight of an attribute using the PSO so as to give effect to the resulting output. This method is then applied to the LVQ-Classifier and tested on the 3 datasets obtained from the UCI Machine Learning repository. Then an accuracy analysis will be generated by two approaches. The first approach using LVQ1, referred to as LVQ-Classifier and the second approach referred to as PSOFW-LVQ, is a proposed model. The result shows that the PSO algorithm is capable of finding attribute weights that increase LVQ-classifier accuracy.

Incremental Adaptive Learning Vector Quantization for Character Recognition with Continuous Style Adaptation

Incremental learning enables continuous model adaptation based on a constantly arriving data stream. It is a way relevant to human cognitive system, which learns to predict objects in a changing world. Incremental learning for character recognition is a typical scenario that characters appear sequentially and the font/writing style changes irregularly. In the paper, we investigate how to classify characters incrementally (i.e., input patterns appear once at a time). A reasonable assumption is that adjacent characters from the same font or the same writer share the same style in a short period while style variation occurs in characters printed by different fonts or written by different persons during a long period. The challenging issue here is how to take advantage of the local style consistency and adapt to the continuous style variation as well incrementally. For this purpose, we propose a continuous incremental adaptive learning vector quantization (CIALVQ) method, which incrementally learns a self-adaptive style transfer matrix for mapping input patterns from style-conscious space onto style-free space. After style transformation, this problem is casted into a common character recognition task and an incremental learning vector quantization (ILVQ) classifier is used. In this framework, we consider two learning modes: supervised incremental learning and active incremental learning. In the latter mode, samples receiving low confidence from the classifier are requested class labels. We evaluated the classification performance of CIALVQ in two scenarios, interleaved test-then-train and style-specific classification on NIST hand-printed data sets. The results show that local style consistency improves the accuracies of both two test scenarios, and for both supervised and active incremental learning modes.

A comparative study of finger vein recognition by using Learning Vector Quantization

¾ This paper presents a comparative study of finger vein recognition using various features with Learning Vector Quantization (LVQ) as a classification method. For the purpose of this study, two main features are employed: Scale Invariant Feature Transform (SIFT) and Local Extensive Binary Pattern (LEBP). The other features that formed LEBP features: Local Multilayer Binary Pattern (LmBP) and Local Directional Binary Pattern (LdBP) are also employed. The type of images are also become the base of comparison. The SIFT features will be extracted from two types of images which are grayscale and binary images. The feature that have been extracted become the input for recognition stage. In recognition stage, LVQ classifier is used. LVQ will classify the images into two class which are the recognizable images and non recognizable images. The accuracy, false positive rate (FPR), and true positive rate (TPR) value are used to evaluate the performance of finger vein recognition. The performance result of finger vein recognition becomes the main study for comparison stage. From the experiments result, it can be found which feature is the best for finger vein reconition using LVQ. The performance of finger vein recognition that use SIFT feature from binary images give a slightly better result than uisng LmBP, LdBP, or LEBP feature. The accuracy value could achieve 97,45%, TPR at 0,9000 and FPR at 0,0129.

A New Classification Algorithm: Optimally Generalized Learning Vector Quantization (OGLVQ)

We present a new Generalized Learning Vector Quantization classifier called Optimally Generalized Learning Vector Quantization based on a novel weight-update rule for learning labelled samples. The algorithm attains stable prototype/weight vector dynamics in terms of estimated current and previous weights and their updates. Resulting weight update term is then related to the proximity measure used by Generalized Learning Vector Quantization classifiers. New algorithm and some major counterparts are tested and compared for synthetic and publicly available datasets. Results reveal that new classifier is faster in training and is more successful and robust in classifying test samples of datasets studied than the counterparts it is compared.

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment.

Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a “Learning with privileged information” approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI.

An Experimental Comparison of Modeling Techniques and Combination of Speaker – Specific Information from Different Languages for Multilingual Speaker Identification

AbstractMost of the state-of-the-art speaker identification systems work on a monolingual (preferably English) scenario. Therefore, English-language autocratic countries can use the system efficiently for speaker recognition. However, there are many countries, including India, that are multilingual in nature. People in such countries have habituated to speak multiple languages. The existing speaker identification system may yield poor performance if a speaker’s train and test data are in different languages. Thus, developing a robust multilingual speaker identification system is an issue in many countries. In this work, an experimental evaluation of the modeling techniques, including self-organizing map (SOM), learning vector quantization (LVQ), and Gaussian mixture model-universal background model (GMM-UBM) classifiers for multilingual speaker identification, is presented. The monolingual and crosslingual speaker identification studies are conducted using 50 speakers of our own database. It is observed from the experimental results that the GMM-UBM classifier gives better identification performance than the SOM and LVQ classifiers. Furthermore, we propose a combination of speaker-specific information from different languages for crosslingual speaker identification, and it is observed that the combination feature gives better performance in all the crosslingual speaker identification experiments.

Learning vector quantization classifiers for ROC-optimization

This paper proposes a variant of the generalized learning vector quantizer (GLVQ) optimizing explicitly the area under the receiver operating characteristics (ROC) curve for binary classification problems instead of the classification accuracy, which is frequently not appropriate for classifier evaluation. This is particularly important in case of overlapping class distributions, when the user has to decide about the trade-off between high true-positive and good false-positive performance. The model keeps the idea of learning vector quantization based on prototypes by stochastic gradient descent learning. For this purpose, a GLVQ-based cost function is presented, which describes the area under the ROC-curve in terms of the sum of local discriminant functions. This cost function reflects the underlying rank statistics in ROC analysis being involved into the design of the prototype based discriminant function. The resulting learning scheme for the prototype vectors uses structured inputs, i.e. ordered pairs of data vectors of both classes.