Impact of Antibody Panel Size on Classification Accuracy

2011 ◽  
Vol 45 (1) ◽  
pp. 85-90
Author(s):  
Inese Polaka ◽  
Arkady Borisov
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Size Reduction ◽  
Data Sets ◽  
Biomedical Data ◽  
Classification Methods ◽  
Panel Size ◽  
Antibody Panel

Impact of Antibody Panel Size on Classification Accuracy This paper experimentally studies the influence of antibody panel size reduction on classification results. The presented study includes four classification methods and five feature evaluators that are applied to five different biomedical data sets with large dimensionality (1200 features). The behaviour of the classifiers in these data sets is examined to reveal overall trends of dimensionality reduction impact on classification accuracy.

scholarly journals Partitioned Relief-F Method for Dimensionality Reduction of Hyperspectral Images

2020 ◽  
Vol 12 (7) ◽  
pp. 1104
Author(s):  
Jiansi Ren ◽  
Ruoxiang Wang ◽  
Gang Liu ◽  
Ruyi Feng ◽  
Yuanni Wang ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Principal Component ◽  
Data Sets ◽  
Improved Method ◽  
Remote Sensing Images ◽  
Reduction Methods ◽  
Hyperspectral Remote Sensing Images ◽  
Partitioning Strategy

The classification of hyperspectral remote sensing images is difficult due to the curse of dimensionality. Therefore, it is necessary to find an effective way to reduce the dimensions of such images. The Relief-F method has been introduced for supervising dimensionality reduction, but the band subset obtained by this method has a large number of continuous bands, resulting in a reduction in the classification accuracy. In this paper, an improved method—called Partitioned Relief-F—is presented to mitigate the influence of continuous bands on classification accuracy while retaining important information. Firstly, the importance scores of each band are obtained using the original Relief-F method. Secondly, the whole band interval is divided in an orderly manner, using a partitioning strategy according to the correlation between the bands. Finally, the band with the highest importance score is selected in each sub-interval. To verify the effectiveness of the proposed Partitioned Relief-F method, a classification experiment is performed on three publicly available data sets. The dimensionality reduction methods Principal Component Analysis (PCA) and original Relief-F are selected for comparison. Furthermore, K-Means and Balanced Iterative Reducing and Clustering Using Hierarchies (BIRCH) are selected for comparison in terms of partitioning strategy. This paper mainly measures the effectiveness of each method indirectly, using the overall accuracy of the final classification. The experimental results indicate that the addition of the proposed partitioning strategy increases the overall accuracy of the three data sets by 1.55%, 3.14%, and 0.83%, respectively. In general, the proposed Partitioned Relief-F method can achieve significantly superior dimensionality reduction effects.

scholarly journals Enhancing Histopathological Colorectal Cancer Image Classification by using Convolutional Neural Network

2021 ◽  
Author(s):  
Radwan Al.Shawesh ◽  
Yi Xiang Chen
Keyword(s):  
Colorectal Cancer ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Validation Dataset ◽  
Data Sets ◽  
Classification Methods ◽  
Tumor Diseases ◽  
Histopathological Images ◽  
Death Causes ◽  
Better Than

AbstractColorectal cancer (CRC) also known as bowl cancer is one of the leading death causes worldwide. Early diagnosis has become vital for a successful treatment. Now days with the new advancements in Convolutional Neural networks (CNNs) it’s possible to classify different images of CRC into different classes. Today It is crucial for physician to take advantage of the new advancement’s in deep learning, since classification methods are becoming more and more accurate and efficient. In this study, we introduce a method to improve the classification accuracy from previous studies that used the National Center for Tumor diseases (NCT) data sets. We adapt the ResNet-50 model in our experiment to classify the CRC histopathological images. Furthermore, we utilize transfer learning and fine-tunning techniques to improve the accuracy. Our Experiment results show that ResNet_50 network is the best CNN architecture so far for classifying CRC histopathological images on the NCT Biobank open source dataset. In addition to that using transfer learning allow us to obtain 97.7% accuracy on the validation dataset, which is better than all previous results we found in literature.

ER rule classifier with an optimization operator recommendation

2021 ◽  
pp. 1-13
Author(s):  
Xiaoyan Wang ◽  
Jianbin Sun ◽  
Qingsong Zhao ◽  
Yaqian You ◽  
Jiang Jiang
Keyword(s):  
Classification Accuracy ◽  
Comprehensive Analysis ◽  
Small Sample ◽  
Empirical Knowledge ◽  
Classification Methods ◽  
Turbofan Engine ◽  
Training Samples ◽  
Optimization Operator ◽  
Recommendation Strategy

It is difficult for many classic classification methods to consider expert experience and classify small-sample datasets well. The evidential reasoning rule (ER rule) classifier can solve these problems. The ER rule has strong processing and comprehensive analysis abilities for diversified mixed information and can solve problems with expert experience effectively. Moreover, the initial parameters of the classifier constructed based on the ER rule can be set according to empirical knowledge instead of being trained by a large number of samples, which can help the classifier classify small-sample datasets well. However, the initial parameters of the ER rule classifier need to be optimized, and choosing the best optimization algorithm is still a challenge. Considering these problems, the ER rule classifier with an optimization operator recommendation is proposed in this paper. First, the initial ER rule classifier is constructed based on training samples and expert experience. Second, the adjustable parameters are optimized, in which the optimization operator recommendation strategy is applied to select the best algorithm by partial samples, and then experiments with full samples are carried out. Finally, a case study on a turbofan engine degradation simulation dataset is carried out, and the results indicate that the ER rule classifier has a higher classification accuracy than other classic classifiers, which demonstrates the capability and effectiveness of the proposed ER rule classifier with an optimization operator recommendation.

scholarly journals Fugl-Meyer hand motor imagination recognition for brain–computer interfaces using only fNIRS

2021 ◽  
Author(s):  
Chenguang Li ◽  
Hongjun Yang ◽  
Long Cheng
Keyword(s):  
Classification Accuracy ◽  
Near Infrared ◽  
Support Vector ◽  
Classification Methods ◽  
Experimental Paradigm ◽  
Functional Near Infrared Spectroscopy ◽  
Machine Method ◽  
Physiological Signal ◽  
Computer Interfaces ◽  
Mode Decomposition

AbstractAs a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

scholarly journals Review of classification methods on unbalanced data sets

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Le Wang ◽  
Meng Han ◽  
Xiaojuan Li ◽  
Ni Zhang ◽  
Haodong Cheng
Keyword(s):  
Unbalanced Data ◽  
Data Sets ◽  
Classification Methods

scholarly journals A model for spectroscopic food sample analysis using data sonification

2021 ◽  
Author(s):  
Hsein Kew
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Reduction Method ◽  
Real Life ◽  
Principal Component ◽  
Relevant Information ◽  
Analysis Model ◽  
Linear Discriminant ◽  
Audio Output ◽  
Dimensionality Reduction Method

AbstractIn this paper, we propose a method to generate an audio output based on spectroscopy data in order to discriminate two classes of data, based on the features of our spectral dataset. To do this, we first perform spectral pre-processing, and then extract features, followed by machine learning, for dimensionality reduction. The features are then mapped to the parameters of a sound synthesiser, as part of the audio processing, so as to generate audio samples in order to compute statistical results and identify important descriptors for the classification of the dataset. To optimise the process, we compare Amplitude Modulation (AM) and Frequency Modulation (FM) synthesis, as applied to two real-life datasets to evaluate the performance of sonification as a method for discriminating data. FM synthesis provides a higher subjective classification accuracy as compared with to AM synthesis. We then further compare the dimensionality reduction method of Principal Component Analysis (PCA) and Linear Discriminant Analysis in order to optimise our sonification algorithm. The results of classification accuracy using FM synthesis as the sound synthesiser and PCA as the dimensionality reduction method yields a mean classification accuracies of 93.81% and 88.57% for the coffee dataset and the fruit puree dataset respectively, and indicate that this spectroscopic analysis model is able to provide relevant information on the spectral data, and most importantly, is able to discriminate accurately between the two spectra and thus provides a complementary tool to supplement current methods.

scholarly journals Towards Application of One-Class Classification Methods to Medical Data

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Itziar Irigoien ◽  
Basilio Sierra ◽  
Concepción Arenas
Keyword(s):  
State Of The Art ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Sets ◽  
Biomedical Data ◽  
Vector Data ◽  
Target Class ◽  
Tumor Recognition ◽  
One Class Classification

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques—Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description—using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.

GENE SELECTION USING LOGISTIC REGRESSIONS BASED ON AIC, BIC AND MDL CRITERIA

2005 ◽  
Vol 01 (01) ◽  
pp. 129-145 ◽  
Author(s):  
XIAOBO ZHOU ◽  
XIAODONG WANG ◽  
EDWARD R. DOUGHERTY
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Gene Selection ◽  
Information Criterion ◽  
Cancer Classification ◽  
Data Sets ◽  
Classification Methods ◽  
Gene Expressions ◽  
Experimental Conditions

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables (gene expressions) and the small number of experimental conditions. Many gene-selection and classification methods have been proposed; however most of these treat gene selection and classification separately, and not under the same model. We propose a Bayesian approach to gene selection using the logistic regression model. The Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the minimum description length (MDL) principle are used in constructing the posterior distribution of the chosen genes. The same logistic regression model is then used for cancer classification. Fast implementation issues for these methods are discussed. The proposed methods are tested on several data sets including those arising from hereditary breast cancer, small round blue-cell tumors, lymphoma, and acute leukemia. The experimental results indicate that the proposed methods show high classification accuracies on these data sets. Some robustness and sensitivity properties of the proposed methods are also discussed. Finally, mixing logistic-regression based gene selection with other classification methods and mixing logistic-regression-based classification with other gene-selection methods are considered.

Multi-Instance Dimensionality Reduction via Sparsity and Orthogonality

2018 ◽  
Vol 30 (12) ◽  
pp. 3281-3308
Author(s):  
Hong Zhu ◽  
Li-Zhi Liao ◽  
Michael K. Ng
Keyword(s):  
Dimensionality Reduction ◽  
Optimization Problem ◽  
Augmented Lagrangian ◽  
Main Idea ◽  
Real Data ◽  
Learning Performance ◽  
High Dimensional ◽  
Data Sets ◽  
Outer Loop ◽  
Orthogonality Constraints

We study a multi-instance (MI) learning dimensionality-reduction algorithm through sparsity and orthogonality, which is especially useful for high-dimensional MI data sets. We develop a novel algorithm to handle both sparsity and orthogonality constraints that existing methods do not handle well simultaneously. Our main idea is to formulate an optimization problem where the sparse term appears in the objective function and the orthogonality term is formed as a constraint. The resulting optimization problem can be solved by using approximate augmented Lagrangian iterations as the outer loop and inertial proximal alternating linearized minimization (iPALM) iterations as the inner loop. The main advantage of this method is that both sparsity and orthogonality can be satisfied in the proposed algorithm. We show the global convergence of the proposed iterative algorithm. We also demonstrate that the proposed algorithm can achieve high sparsity and orthogonality requirements, which are very important for dimensionality reduction. Experimental results on both synthetic and real data sets show that the proposed algorithm can obtain learning performance comparable to that of other tested MI learning algorithms.

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