Small Sample Size Classification Research Articles

An Automatic Evaluation Method for Parkinson's Dyskinesia Using Finger Tapping Video for Small Samples

PurposeThe assessment of dyskinesia in Parkinson's disease (PD) based on Artificial Intelligence technology is a significant and challenging task. Based on the representative action of finger tapping, this paper designs an automatic evaluation method by deep learning technology to provide big data screening and preliminary evaluation for potential patients with PD. Presently, doctors use the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to assess the severity of patients. This method is time-consuming and laborious, and there are subjective differences. The evaluation method based on sensor equipment is also widely used, but it is expensive and needs professional guidance, which is not suitable for remote evaluation and patient self-examination. Additionally, it is difficult to collect patient data for medical research, so it is important to find an objective and automatic assessment method for Parkinson's dyskinesia based on small samples.MethodsIn this study, we designed an automatic evaluation method combining manual features and convolutional neural network (CNN) suitable for small sample classification. Based on the finger tapping video of Parkinson's patients, we used the pose estimation model to obtain the action skeleton information and calculated the feature data. We then use the 5-folds cross validation training model to achieve an optimum trade-off between bias and variance, and finally make a multi-class prediction through fully connected network (FCN).ResultsOur proposed method achieves the current optimal accuracy of 79.7% in this research. We have compared with the latest methods of related research, and our method is superior to them in terms of accuracy, number of parameters and floating point operations (FLOPs).ConclusionThe proposed method does not require patients to wear sensor devices and has clear advantages in providing early screening and remote clinical evaluation for patients with PD. Simultaneously, the proposed method using motion feature data to train the CNN model obtains the optimal accuracy, effectively solves the problem of limited data acquisition in medicine and provides a novice proposal for a small sample size classification.

Optimization Performance Comparison of Three Different Group Intelligence Algorithms on a SVM for Hyperspectral Imagery Classification

Group intelligence algorithms have been widely used in support vector machine (SVM) parameter optimization due to their obvious characteristics of strong parallel processing ability, fast optimization, and global optimization. However, few studies have made optimization performance comparisons of different group intelligence algorithms on SVMs, especially in terms of their application to hyperspectral remote sensing classification. In this paper, we compare the optimization performance of three different group intelligence algorithms that were run on a SVM in terms of five aspects by using three hyperspectral images (one each of the Indian Pines, University of Pavia, and Salinas): the stability to parameter settings, convergence rate, feature selection ability, sample size, and classification accuracy. Particle swarm optimization (PSO), genetic algorithms (GAs), and artificial bee colony (ABC) algorithms are the three group intelligence algorithms. Our results showed the influence of these three optimization algorithms on the C-parameter optimization of the SVM was less than their influence on the σ-parameter. The convergence rate, the number of selected features, and the accuracy of the three group intelligence algorithms were statistically significant different at the p = 0.01 level. The GA algorithm could compress more than 70% of the original data and it was the least affected by sample size. GA-SVM had the highest average overall accuracy (91.77%), followed by ABC-SVM (88.73%), and PSO-SVM (86.65%). Especially, in complex scenes (e.g., the Indian Pines image), GA-SVM showed the highest classification accuracy (87.34%, which was 8.23% higher than ABC-SVM and 16.42% higher than PSO-SVM) and the best stability (the standard deviation of its classification accuracy was 0.82%, which was 5.54% lower than ABC-SVM, and 21.63% lower than PSO-SVM). Therefore, when compared with the ABC and PSO algorithms, the GA had more advantages in terms of feature band selection, small sample size classification, and classification accuracy.

An Improved Algorithm for Imbalanced Data and Small Sample Size Classification

Traditional classification algorithms perform not very well on imbalanced data sets and small sample size. To deal with the problem, a novel method is proposed to change the class distribution through adding virtual samples, which are generated by the windowed regression over-sampling (WRO) method. The proposed method WRO not only reflects the additive effects but also reflects the multiplicative effect between samples. A comparative study between the proposed method and other over-sampling methods such as synthetic minority over-sampling technique (SMOTE) and borderline over-sampling (BOS) on UCI datasets and Fourier transform infrared spectroscopy (FTIR) data set is provided. Experimental results show that the WRO method can achieve better performance than other methods.

META-DES: A dynamic ensemble selection framework using meta-learning

Dynamic ensemble selection systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. This is achieved by defining a criterion to measure the level of competence of a base classifier, such as, its accuracy in local regions of the feature space around the query instance. However, using only one criterion about the behavior of a base classifier is not sufficient to accurately estimate its level of competence. In this paper, we present a novel dynamic ensemble selection framework using meta-learning. We propose five distinct sets of meta-features, each one corresponding to a different criterion to measure the level of competence of a classifier for the classification of input samples. The meta-features are extracted from the training data and used to train a meta-classifier to predict whether or not a base classifier is competent enough to classify an input instance. During the generalization phase, the meta-features are extracted from the query instance and passed down as input to the meta-classifier. The meta-classifier estimates, whether a base classifier is competent enough to be added to the ensemble. Experiments are conducted over several small sample size classification problems, i.e., problems with a high degree of uncertainty due to the lack of training data. Experimental results show that the proposed meta-learning framework greatly improves classification accuracy when compared against current state-of-the-art dynamic ensemble selection techniques.

Locating facial landmarks by support vector machine-based active shape model

Active shape model (ASM) plays an important role in face research such as face recognition, pose estimation and gaze estimation. A crucial step of the common ASM is finding a new position for each facial landmark at each iteration. Mahalanobis distance minimisation is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark following a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a novel method support vector machine-based active shape model (SVMBASM) is proposed for this task. It approaches the finding task as a small sample size classification problem. Moreover, considering the poor classification performance caused by the imbalanced dataset which contains more negative instances (incorrect candidates for new position) than positive instances (correct candidates for new position), a multi-class classification framework is further proposed. Performance evaluation on Shanghai Jiao Tong University face database shows that the proposed SVMBASM outperforms the original ASM in terms of the average error and average frequency of convergence.

Feature Extractions for Small Sample Size Classification Problem

Much research has shown that the definitions of within-class and between-class scatter matrices and regularization technique are the key components to design a feature extraction for small sample size problems. In this paper, we illustrate the importance of another key component, eigenvalue decomposition method, and a new regularization technique was proposed. In the hyperspectral image experiment, the effects of these three components of feature extraction are explored on ill-posed and poorly posed conditions. The experimental results show that different regularization methods need to cooperate with different eigenvalue decomposition methods to reach the best performance, the proposed regularization method, regularized feature extraction (RFE) outperform others, and the best feature extraction for a small sample size classification problem is RFE with nonparametric weighted scatter matrices

A covariance estimator for small sample size classification problems and its application to feature extraction

A key to successful classification of multivariate data is the defining of an accurate quantitative model of each class. This is especially the case when the dimensionality of the data is high, and the problem is exacerbated when the number of training samples is limited. For the commonly used quadratic maximum-likelihood classifier, the class mean vectors and covariance matrices are required and must be estimated from the available training samples. In high dimensional cases, it has been found that feature extraction methods are especially useful, so as to transform the problem to a lower dimensional space without loss of information, however, here too class statistics estimation error is significant. Finding a suitable regularized covariance estimator is a way to mitigate these estimation error effects. The main purpose of this work is to find an improved regularized covariance estimator of each class with the advantages of Leave-One-Out Covariance Estimator (LOOC) and Bayesian LOOC (BLOOC). Besides, using the proposed covariance estimator to improve the linear feature extraction methods when the multivariate data is singular or nearly so is demonstrated. This work is specifically directed at analysis methods for hyperspectral remote sensing data.

Influence of Projection Pursuit on Classification Errors: Computer Simulation Results

Influence of projection pursuit on classification errors and estimates of a posteriori prob- abilities from the sample is considered. Observed random variable is supposed to satisfy a multidi- mensional Gaussian mixture model. Presented computer simulation results show that for compar- atively small sample size classification using projection pursuit algorithm gives better accuracy of estimates of a posteriori probabilities and less classification error.