Small-Sample Classification

2015 ◽  
pp. 77-101 ◽  
Author(s):  
Lori A. Dalton ◽  
Edward R. Dougherty
Keyword(s):  
Small Sample ◽  
Sample Classification

scholarly journals An Ensemble Classification Method for High-Dimensional Data Using Neighborhood Rough Set

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jing Zhang ◽  
Guang Lu ◽  
Jiaquan Li ◽  
Chuanwen Li
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Small Sample Size ◽  
High Dimensional Data ◽  
Classification Performance ◽  
Small Sample ◽  
Ensemble Classification ◽  
High Dimensional ◽  
Sample Classification ◽  
Neighborhood Rough Set

Mining useful knowledge from high-dimensional data is a hot research topic. Efficient and effective sample classification and feature selection are challenging tasks due to high dimensionality and small sample size of microarray data. Feature selection is necessary in the process of constructing the model to reduce time and space consumption. Therefore, a feature selection model based on prior knowledge and rough set is proposed. Pathway knowledge is used to select feature subsets, and rough set based on intersection neighborhood is then used to select important feature in each subset, since it can select features without redundancy and deals with numerical features directly. In order to improve the diversity among base classifiers and the efficiency of classification, it is necessary to select part of base classifiers. Classifiers are grouped into several clusters by k-means clustering using the proposed combination distance of Kappa-based diversity and accuracy. The base classifier with the best classification performance in each cluster will be selected to generate the final ensemble model. Experimental results on three Arabidopsis thaliana stress response datasets showed that the proposed method achieved better classification performance than existing ensemble models.

scholarly journals OSLNet: Deep Small-Sample Classification With an Orthogonal Softmax Layer

2020 ◽  
Vol 29 ◽  
pp. 6482-6495 ◽  
Author(s):  
Xiaoxu Li ◽  
Dongliang Chang ◽  
Zhanyu Ma ◽  
Zheng-Hua Tan ◽  
Jing-Hao Xue ◽  
...  
Keyword(s):  
Small Sample ◽  
Sample Classification

scholarly journals The Illusion of Distribution-Free Small-Sample Classification in Genomics

2011 ◽  
Vol 12 (5) ◽  
pp. 333-341 ◽  
Author(s):  
Edward R. Dougherty ◽  
Amin Zollanvari ◽  
Ulisses M. Braga-Neto
Keyword(s):  
Small Sample ◽  
Distribution Free ◽  
Sample Classification

Small sample classification for hyperspectral imagery using temporal convolution and attention mechanism

2021 ◽  
Vol 12 (5) ◽  
pp. 510-519
Author(s):  
Kuiliang Gao ◽  
Xuchu Yu ◽  
Xiong Tan ◽  
Bing Liu ◽  
Yifan Sun
Keyword(s):  
Hyperspectral Imagery ◽  
Small Sample ◽  
Attention Mechanism ◽  
Sample Classification

scholarly journals A novel strategy for driving car brain–computer interfaces: Discrimination of EEG-based visual-motor imagery

2021 ◽  
Vol 12 (1) ◽  
pp. 482-493
Author(s):  
Zhouzhou Zhou ◽  
Anmin Gong ◽  
Qian Qian ◽  
Lei Su ◽  
Lei Zhao ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Small Sample ◽  
Support Vector ◽  
Hilbert Huang Transform ◽  
Sample Classification ◽  
Eeg Data ◽  
Visual Motor ◽  
Novel Strategy

Abstract A brain–computer interface (BCI) based on kinesthetic motor imagery has a potential of becoming a groundbreaking technology in a clinical setting. However, few studies focus on a visual-motor imagery (VMI) paradigm driving BCI. The VMI-BCI feature extraction methods are yet to be explored in depth. In this study, a novel VMI-BCI paradigm is proposed to execute four VMI tasks: imagining a car moving forward, reversing, turning left, and turning right. These mental strategies can naturally control a car or robot to move forward, backward, left, and right. Electroencephalogram (EEG) data from 25 subjects were collected. After the raw EEG signal baseline was corrected, the alpha band was extracted using bandpass filtering. The artifacts were removed by independent component analysis. Then, the EEG average instantaneous energy induced by VMI (VMI-EEG) was calculated using the Hilbert–Huang transform (HHT). The autoregressive model was extracted to construct a 12-dimensional feature vector to a support vector machine suitable for small sample classification. This was classified into two-class tasks: visual imagination of driving the car forward versus reversing, driving forward versus turning left, driving forward versus turning right, reversing versus turning left, reversing versus turning right, and turning left versus turning right. The results showed that the average classification accuracy of these two-class tasks was 62.68 ± 5.08%, and the highest classification accuracy was 73.66 ± 6.80%. The study showed that EEG features of O1 and O2 electrodes in the occipital region extracted by HHT were separable for these VMI tasks.

A new semi-supervised algorithm combined with MCICA optimizing SVM for motion imagination EEG classification

2021 ◽  
Vol 25 (4) ◽  
pp. 863-877
Author(s):  
Xuemin Tan ◽  
Chao Guo ◽  
Tao Jiang ◽  
Kechang Fu ◽  
Nan Zhou ◽  
...  
Keyword(s):  
Objective Function ◽  
Cross Validation ◽  
Small Sample ◽  
Support Vector ◽  
Optimal Parameters ◽  
Classification Problems ◽  
Training Process ◽  
Data Set ◽  
Eeg Classification ◽  
Sample Classification

This paper proposed a new semi-supervised algorithm combined with Mutual-cross Imperial Competition Algorithm (MCICA) optimizing Support Vector Machine (SVM) for motion imagination EEG classification, which not only reduces the tedious and time-consuming training process and enhances the adaptability of Brain Computer Interface (BCI), but also utilizes the MCICA to optimize the parameters of SVM in the semi-supervised process. This algorithm combines mutual information and cross validation to construct objective function in the semi-supervised training process, and uses the constructed objective function to establish the semi-supervised model of MCICA for optimizing the parameters of SVM, and finally applies the selected optimal parameters to the data set Iva of 2005 BCI competition to verify its effectiveness. The results showed that the proposed algorithm is effective in optimizing parameters and has good robustness and generalization in solving small sample classification problems.

Sign in / Sign up

Export Citation Format

Share Document