Journal articles: 'Classification of samples'

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Lindsay, K. S., I. Floyd, and R. Swan. "Classification of azoospermic samples." Lancet 345, no. 8965 (June 1995): 1642. http://dx.doi.org/10.1016/s0140-6736(95)90150-7.

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Ivanova, Elena. "Classification of samples for comparative research." Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia 2019, no. 4 (December 25, 2019): 153–57. http://dx.doi.org/10.35750/2071-8284-2019-4-153-157.

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The article presents the classification of samples for comparative research, the study of which will contribute to the correct choice of a tactical method of obtaining them. The material also contains an analysis of the scientific literature related to the classification and systematization of the category in question; alternative grounds for dividing into groups are proposed. Classifying comparative samples, the author focuses on certain problems that practitioners face. Namely: the possibility of obtaining samples for a comparative research before the initiation of a criminal case, the validity of the use of coercion to obtain them, as well as the possibility of using comparative material obtained in the framework of operational search activities for expert studies. In preparing the article, general scientific methods of empirical knowledge (comparison, description), general logical methods of analysis, synthesis, generalization, classification, as well as methods of system-structural analysis were used. The purpose of the study of the problems of obtaining samples is to provide a systematic classification of this category in order to improve the quality of crime investigation. The conclusions made in the article on issues related to obtaining comparative samples at the stage of pre-investigation check and the possibility of using samples obtained during operational search activities are logically presented in the proposed classification.

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Telaar, Anna, Dirk Repsilber, and Gerd Nürnberg. "Biomarker discovery: classification using pooled samples." Computational Statistics 28, no. 1 (January 19, 2012): 67–106. http://dx.doi.org/10.1007/s00180-011-0302-0.

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Chen, Xia, Guoxian Yu, Qiaoyu Tan, and Jun Wang. "Weighted samples based semi-supervised classification." Applied Soft Computing 79 (June 2019): 46–58. http://dx.doi.org/10.1016/j.asoc.2019.03.005.

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Hansen, Lucas, and Marco Flôres Ferrão. "Classification of Milk Samples Using CART." Food Analytical Methods 13, no. 1 (April 6, 2019): 13–20. http://dx.doi.org/10.1007/s12161-019-01493-9.

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Al-Hamed, Shareef. "Comparison Between the Classical Classification and Digital Classification for Selected Samples of Igneous and Carbonate Rocks." Iraqi Geological Journal 54, no. 1C (March 31, 2021): 16–29. http://dx.doi.org/10.46717/igj.54.1c.2ms-2021-03-22.

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As igneous rocks have widely chemical and mineralogical compositions, there are many ways to classify these rocks. These ways are classical approved methods to give a reliable classification and nomenclature of rocks. Some igneous rocks may be classified by digital image processing to assist in classical methods. Five igneous samples were cut, prepared of thin sections, and polished to classify them by classical methods and digital image processing by ENVI software. Moreover, part of these samples crushed an analysis of major oxides. The current igneous samples have referred to the basic and mesocratic rocks based on the classical methods and this has corresponded to ENVI software. The igneous samples have reflected the leucogabbros when classify them by classical and ENVI classifications, except the G5 sample, which has been referred to as gabbro by ENVI. There is a clear similarity between the classical and ENVI classifications. ENVI classification is a reliable classification to assist the classical methods in the nomenclature of igneous rocks, especially, plutonic rocks, it can be also applied to thin sections of volcanic rocks to classify and nomenclature classification by ENVI has been applied on fifty thin sections of limestones to identify microfacies which are classified beforehand by classical (optical) classification. According to optical classification, microfacies have classified as mudstone, wackestone, packstone, and grainstone. When the digital classification is applied to them, there is no grainstone texture found in these them. Digital thin sections, where the true name of these microfacies is packstone. Therefore, the positive sides of the digital image processing by ENVI software appeared and contrasted to the optical classification which contained some mistakes when applied to the nomenclature of these microfacies.

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Qiu, Minna, Jian Zhang, Jiayan Yang, and Liying Ye. "Fusing Two Kinds of Virtual Samples for Small Sample Face Recognition." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/280318.

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Face recognition has become a very active field of biometrics. Different pictures of the same face might include various changes of expressions, poses, and illumination. However, a face recognition system usually suffers from the problem that nonsufficient training samples cannot convey these possible changes effectively. The main reason is that a system has only limited storage space and limited time to capture training samples. Many previous literatures ignored the problem of nonsufficient training samples. In this paper, we overcome the insufficiency of training sample size problem by fusing two kinds of virtual samples and the original samples to perform small sample face recognition. The two used kinds of virtual samples are mirror faces and symmetrical faces. Firstly, we transform the original face image to obtain mirror faces and symmetrical faces. Secondly, we fuse these two kinds of virtual samples to achieve the matching scores between the test sample and each class. Finally, we integrate the matching scores to get the final classification results. We compare the proposed method with the single virtual sample augment methods and the original representation-based classification. The experiments on various face databases show that the proposed scheme achieves the best accuracy among the representation-based classification methods.

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Haaland, David M., Howland D. T. Jones, and Edward V. Thomas. "Multivariate Classification of the Infrared Spectra of Cell and Tissue Samples." Applied Spectroscopy 51, no. 3 (March 1997): 340–45. http://dx.doi.org/10.1366/0003702971940468.

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Infrared microspectroscopy of biopsied canine lymph cells and tissue was performed to investigate the possibility of using IR spectra coupled with multivariate classification methods to classify the samples as normal, hyperplastic, or neoplastic (malignant). IR spectra were obtained in transmission mode through BaF2 windows and in reflection mode from samples prepared on gold-coated microscope slides. Cytology and histopathology samples were prepared by a variety of methods to identify the optimal methods of sample preparation. Cytospinning procedures that yielded a monolayer of cells on the BaF2 windows produced a limited set of IR transmission spectra. These transmission spectra were converted to absorbance and formed the basis for a classification rule that yielded 100% correct classification in a cross-validated context. Classifications of normal, hyperplastic, and neoplastic cell sample spectra were achieved by using both partial least-squares (PLS) and principal component regression (PCR) classification methods. Linear discriminant analysis applied to principal components obtained from the spectral data yielded a small number of misclassifications. PLS weight loading vectors yield valuable qualitative insight into the molecular changes that are responsible for the success of the infrared classification. These successful classification results show promise for assisting pathologists in the diagnosis of cell types and offer future potential for in vivo IR detection of some types of cancer.

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Cao, Feilong, Tenghui Dai, Yongquan Zhang, and Yuanpeng Tan. "Compressed classification learning with Markov chain samples." Neural Networks 50 (February 2014): 90–97. http://dx.doi.org/10.1016/j.neunet.2013.11.008.

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Li, Tze Fen, and Shui-Ching Chang. "Classification on defective items using unidentified samples." Pattern Recognition 38, no. 1 (January 2005): 51–58. http://dx.doi.org/10.1016/j.patcog.2004.05.008.

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Vong, Richard J., Timothy V. Larson, and William H. Zoller. "A multivariate chemical classification of rainwater samples." Chemometrics and Intelligent Laboratory Systems 3, no. 1-2 (February 1988): 99–109. http://dx.doi.org/10.1016/0169-7439(88)80071-6.

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Kianisarkaleh, A., H. Ghassemian, and F. Razzazi. "UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 411–16. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-411-2015.

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Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples exists. This paper illustrates the importance of selecting appropriate unlabeled samples that used in feature extraction methods. Also proposes a new method for unlabeled samples selection using spectral and spatial information. The proposed method has four parts including: PCA, prior classification, posterior classification and sample selection. As hyperspectral image passes these parts, selected unlabeled samples can be used in arbitrary feature extraction methods. The effectiveness of the proposed unlabeled selected samples in unsupervised and semisupervised feature extraction is demonstrated using two real hyperspectral datasets. Results show that through selecting appropriate unlabeled samples, the proposed method can improve the performance of feature extraction methods and increase classification accuracy.

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Zhou, Yong Zheng, Chen Qiang Zang, Li Wei Chen, and Qìng Yu. "Ancient Ceramics Samples Classification Based on Clustering Analysis." Applied Mechanics and Materials 556-562 (May 2014): 3594–97. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3594.

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We research on 44 ancient ceramics made in Jingdezhen through previous dynasties about its classification problem by K-means Cluster and K-medoids Cluster. We got on classification results and analysed the classification effectiveness of different classification methods. The study has the reference value about the identification of the ancient ceramics.

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Arai, Kohei. "Purification of Training Samples for Supervised TM Classification." Journal of the Japan society of photogrammetry and remote sensing 26, Special2 (1987): 53–60. http://dx.doi.org/10.4287/jsprs.26.special2_53.

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Dang, Thanh-Vu, Hoang-Trong Vo, Gwang-Hyun Yu, Ju-Hwan Lee, Huy-Toan Nguyen, and Jin-Young Kim. "Removing Out – Of – Distribution Samples on Classification Task." Korean Institute of Smart Media 9, no. 3 (September 30, 2020): 80–89. http://dx.doi.org/10.30693/smj.2020.9.3.80.

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Molloy, John L., and John R. Sieber. "Classification of microheterogeneity in solid samples using µXRF." Analytical and Bioanalytical Chemistry 392, no. 5 (August 28, 2008): 995–1001. http://dx.doi.org/10.1007/s00216-008-2324-1.

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Adjouadi, Malek, Melvin Ayala, Mercedes Cabrerizo, Nuannuan Zong, Gabriel Lizarraga, and Mark Rossman. "Classification of Leukemia Blood Samples Using Neural Networks." Annals of Biomedical Engineering 38, no. 4 (December 15, 2009): 1473–82. http://dx.doi.org/10.1007/s10439-009-9866-z.

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Liu, Yiguang, Xiaochun Cao, and Jian Guo Liu. "Classification using distances from samples to linear manifolds." Pattern Analysis and Applications 16, no. 3 (October 30, 2011): 417–30. http://dx.doi.org/10.1007/s10044-011-0242-x.

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Chuang, Ching-Ching, Wu-Che Wen, and Shuenn-Jyi Sheu. "Classification of Aurantii Fructus samples by multivariate analysis." Journal of Separation Science 30, no. 12 (August 2007): 1827–32. http://dx.doi.org/10.1002/jssc.200700016.

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Arkok, Bassam Sulaiman, and Akram Mohammed Zeki. "Classification of Quranic topics based on imbalanced classification." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (May 1, 2021): 678. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp678-687.

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Imbalanced classification techniques have been applied widely in the field of data mining. It is used to classify the imbalanced classes that are not equal in the number of samples. The problem of imbalanced classes is that the classification performance tends to the class with more samples while the class with few samples will obtain poor performance. This problem can be occurred in the Qur’anic classification due to the different number of verses. Many studies classified Qur’anic verses, which depended on the traditional classification. However, no study classified Qur’anic topics based on the techniques of imbalanced classification. Therefore, this paper aims to apply the methods of imbalanced classification as synthetic minority over-sampling technique (SMOTE), random over sample (ROS), and random under sample (RUS) methods to classify the Qur’anic topics that are imbalanced. Many metrics were used in this research to evaluate the experimental results. These metrics are sensitivity/recall, specificity, overall accuracy, F-Measure, G-mean, and matthews correlation coefficient (MCC). The results showed that the Quranic classification performance improved when imbalanced classification techniques were applied

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Tian, Chunwei, Guanglu Sun, Qi Zhang, Weibing Wang, Teng Chen, and Yuan Sun. "Integrating Sparse and Collaborative Representation Classifications for Image Classification." International Journal of Image and Graphics 17, no. 02 (April 2017): 1750007. http://dx.doi.org/10.1142/s0219467817500073.

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Collaborative representation classification (CRC) is an important sparse method, which is easy to carry out and uses a linear combination of training samples to represent a test sample. CRC method utilizes the offset between representation result of each class and the test sample to implement classification. However, the offset usually cannot well express the difference between every class and the test sample. In this paper, we propose a novel representation method for image recognition to address the above problem. This method not only fuses sparse representation and CRC method to improve the accuracy of image recognition, but also has novel fusion mechanism to classify images. The implementations of the proposed method have the following steps. First of all, it produces collaborative representation of the test sample. That is, a linear combination of all the training samples is first determined to represent the test sample. Then, it gets the sparse representation classification (SRC) of the test sample. Finally, the proposed method respectively uses CRC and SRC representations to obtain two kinds of scores of the test sample and fuses them to recognize the image. The experiments of face recognition show that the combination of CRC and SRC has satisfactory performance for image classification.

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Salem, Omar A. M., and Liwei Wang. "Fuzzy Mutual Information Feature Selection Based on Representative Samples." International Journal of Software Innovation 6, no. 1 (January 2018): 58–72. http://dx.doi.org/10.4018/ijsi.2018010105.

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Building classification models from real-world datasets became a difficult task, especially in datasets with high dimensional features. Unfortunately, these datasets may include irrelevant or redundant features which have a negative effect on the classification performance. Selecting the significant features and eliminating undesirable features can improve the classification models. Fuzzy mutual information is widely used feature selection to find the best feature subset before classification process. However, it requires more computation and storage space. To overcome these limitations, this paper proposes an improved fuzzy mutual information feature selection based on representative samples. Based on benchmark datasets, the experiments show that the proposed method achieved better results in the terms of classification accuracy, selected feature subset size, storage, and stability.

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Wedyan, Mohammad, Alessandro Crippa, and Adel Al-Jumaily. "A Novel Virtual Sample Generation Method to Overcome the Small Sample Size Problem in Computer Aided Medical Diagnosing." Algorithms 12, no. 8 (August 9, 2019): 160. http://dx.doi.org/10.3390/a12080160.

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Deep neural networks are successful learning tools for building nonlinear models. However, a robust deep learning-based classification model needs a large dataset. Indeed, these models are often unstable when they use small datasets. To solve this issue, which is particularly critical in light of the possible clinical applications of these predictive models, researchers have developed approaches such as virtual sample generation. Virtual sample generation significantly improves learning and classification performance when working with small samples. The main objective of this study is to evaluate the ability of the proposed virtual sample generation to overcome the small sample size problem, which is a feature of the automated detection of a neurodevelopmental disorder, namely autism spectrum disorder. Results show that our method enhances diagnostic accuracy from 84%–95% using virtual samples generated on the basis of five actual clinical samples. The present findings show the feasibility of using the proposed technique to improve classification performance even in cases of clinical samples of limited size. Accounting for concerns in relation to small sample sizes, our technique represents a meaningful step forward in terms of pattern recognition methodology, particularly when it is applied to diagnostic classifications of neurodevelopmental disorders. Besides, the proposed technique has been tested with other available benchmark datasets. The experimental outcomes showed that the accuracy of the classification that used virtual samples was superior to the one that used original training data without virtual samples.

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Mohamed, Elfadil Abdalla, Fathi H. Saad, and Omer I. E. Mohamed. "Effects of Classification Techniques on Medical Reports Classification." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 2 (April 16, 2014): 4206–21. http://dx.doi.org/10.24297/ijct.v13i2.2906.

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Text classification is the process of assigning pre-defined category labels to documents based on what a classifications has learned from training examples. This paper investigates the partially supervised classification approach in the medical field. The approaches that have been evaluated include Rocchio, NaÃ¯ve Bayesian (NB), Spy, Support vector machine (SVM), and Expectation Maximization (EM). A combination of these methods has been conducted.Â The experimental result showed that the combination which uses EM in step 2 is always produces better results than those uses SVM using small set of training samples. We also found that reducing the features based on tf-tdf values is decreasing the classification performance dramatically. Moreover, reducing the features based on their frequencies improve the classification performance significantly while also increasing efficiency, but it may require some experimentationÂ

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Wang, Wenning, Xuebin Liu, and Xuanqin Mou. "Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification." Remote Sensing 13, no. 4 (February 3, 2021): 547. http://dx.doi.org/10.3390/rs13040547.

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For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.

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Akond, Zobaer, Mohammad Nazmol Hasan, Md Jahangir Alam, Munirul Alam, and Md Nurul Haque Mollah. "Classification of Functional Metagenomes Recovered from Different Environmental Samples." Bioinformation 15, no. 1 (January 31, 2019): 26–31. http://dx.doi.org/10.6026/97320630015026.

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Al-mamory, Safaa O. "On the Enhancement of Classification Algorithms Using Biased Samples." Inteligencia Artificial 22, no. 64 (October 24, 2019): 36–46. http://dx.doi.org/10.4114/intartif.vol22iss64pp36-46.

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Classification algorithms' performance could be enhanced by selecting many representative points to be included in the training sample. In this paper, a new border and rare biased sampling (BRBS) scheme is proposed by assigning each point in the dataset an importance factor. The importance factor of border points and rare points (i.e. points belong to rare classes) is higher than other points. Then the points are selected to be in the training sample depending on these factors. Including these points in the training sample enhances classifiers experience. The results of experiments on 10 UCI machine learning repository datasets prove that the BRBS algorithm outperforms many sampling algorithms and enhanced the performance of several classification algorithms by about 8%. BRBS is proposed to be easy to configure, covering all points space, and generate a unique samples every time it is executed.

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Zhou, Jianhang, and Bob Zhang. "Collaborative Representation Using Non-Negative Samples for Image Classification." Sensors 19, no. 11 (June 8, 2019): 2609. http://dx.doi.org/10.3390/s19112609.

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Collaborative representation based classification (CRC) is an efficient classifier in image classification. By using l 2 regularization, the collaborative representation based classifier holds competitive performances compared with the sparse representation based classifier using less computational time. However, each of the elements calculated from the training samples are utilized for representation without selection, which can lead to poor performances in some classification tasks. To resolve this issue, in this paper, we propose a novel collaborative representation by directly using non-negative representations to represent a test sample collaboratively, termed Non-negative Collaborative Representation-based Classifier (NCRC). To collect all non-negative collaborative representations, we introduce a Rectified Linear Unit (ReLU) function to perform filtering on the coefficients obtained by l 2 minimization according to CRC’s objective function. Next, we represent the test sample by using a linear combination of these representations. Lastly, the nearest subspace classifier is used to perform classification on the test samples. The experiments performed on four different databases including face and palmprint showed the promising results of the proposed method. Accuracy comparisons with other state-of-art sparse representation-based classifiers demonstrated the effectiveness of NCRC at image classification. In addition, the proposed NCRC consumes less computational time, further illustrating the efficiency of NCRC.

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Zitko, V. "Classification of byzantine glass samples by principal component analysis." Fresenius' Zeitschrift f�r Analytische Chemie 331, no. 6 (1988): 614–15. http://dx.doi.org/10.1007/bf01032536.

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Sayyari, Erfan, Ban Kawas, and Siavash Mirarab. "TADA: phylogenetic augmentation of microbiome samples enhances phenotype classification." Bioinformatics 35, no. 14 (July 2019): i31—i40. http://dx.doi.org/10.1093/bioinformatics/btz394.

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Abstract Motivation Learning associations of traits with the microbial composition of a set of samples is a fundamental goal in microbiome studies. Recently, machine learning methods have been explored for this goal, with some promise. However, in comparison to other fields, microbiome data are high-dimensional and not abundant; leading to a high-dimensional low-sample-size under-determined system. Moreover, microbiome data are often unbalanced and biased. Given such training data, machine learning methods often fail to perform a classification task with sufficient accuracy. Lack of signal is especially problematic when classes are represented in an unbalanced way in the training data; with some classes under-represented. The presence of inter-correlations among subsets of observations further compounds these issues. As a result, machine learning methods have had only limited success in predicting many traits from microbiome. Data augmentation consists of building synthetic samples and adding them to the training data and is a technique that has proved helpful for many machine learning tasks. Results In this paper, we propose a new data augmentation technique for classifying phenotypes based on the microbiome. Our algorithm, called TADA, uses available data and a statistical generative model to create new samples augmenting existing ones, addressing issues of low-sample-size. In generating new samples, TADA takes into account phylogenetic relationships between microbial species. On two real datasets, we show that adding these synthetic samples to the training set improves the accuracy of downstream classification, especially when the training data have an unbalanced representation of classes. Availability and implementation TADA is available at https://github.com/tada-alg/TADA. Supplementary information Supplementary data are available at Bioinformatics online.

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Leung, Chi-Ying. "Classification of dichotomous and continuous variables with incomplete samples." Communications in Statistics - Theory and Methods 23, no. 6 (January 1994): 1581–92. http://dx.doi.org/10.1080/03610929408831341.

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Silvestre, Dario, Italo Zoppis, Francesca Brambilla, Valeria Bellettato, Giancarlo Mauri, and Pierluigi Mauri. "Availability of MudPIT data for classification of biological samples." Journal of Clinical Bioinformatics 3, no. 1 (2013): 1. http://dx.doi.org/10.1186/2043-9113-3-1.

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Chaolin Zhang, Xuesong Lu, and Xuegong Zhang. "Significance of Gene Ranking for Classification of Microarray Samples." IEEE/ACM Transactions on Computational Biology and Bioinformatics 3, no. 3 (July 2006): 312–20. http://dx.doi.org/10.1109/tcbb.2006.42.

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Rathore, Saima, Mutawarra Hussain, and Asifullah Khan. "GECC: Gene Expression Based Ensemble Classification of Colon Samples." IEEE/ACM Transactions on Computational Biology and Bioinformatics 11, no. 6 (November 1, 2014): 1131–45. http://dx.doi.org/10.1109/tcbb.2014.2344655.

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Wang, Dali, Zheng Lu, Yichi Xu, Zi Wang, Anthony Santella, and Zhirong Bao. "Cellular Structure Image Classification With Small Targeted Training Samples." IEEE Access 7 (2019): 148967–74. http://dx.doi.org/10.1109/access.2019.2940161.

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Barceló, Carles, Vera Pawlowsky, and Eric Grunsky. "Classification problems of samples of finite mixtures of compositions." Mathematical Geology 27, no. 1 (January 1995): 129–48. http://dx.doi.org/10.1007/bf02083571.

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Morais, Camilo L. M., Kássio M. G. Lima, Maneesh Singh, and Francis L. Martin. "Tutorial: multivariate classification for vibrational spectroscopy in biological samples." Nature Protocols 15, no. 7 (June 17, 2020): 2143–62. http://dx.doi.org/10.1038/s41596-020-0322-8.

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Marisa, Laetitia, Mira Ayadi, Ralyath Balogoun, Camilla Pilati, Karine Le Malicot, Come Lepage, Jean-François Emile, et al. "Clinical utility of colon cancer molecular subtypes: Validation of two main colorectal molecular classifications on the PETACC-8 phase III trial cohort." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 3509. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.3509.

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3509 Background: The molecular subtyping of colon cancers (CC) has been the subject of several recent publications, leading to an international consensus. The clinical relevance of these molecular classifications remains to be evaluated on large prospective patient cohorts using a tool that can be widely used on formalin-fixed paraffin-embedded (FFPE) samples. Methods: We aimed to evaluate the clinical relevance of two molecular subtyping systems, CMS (Guinney et al. 2015) and CCMST (Marisa et al. 2013), on the PETACC-8 cohort, a randomized phase III trial comparing adjuvant FOLFOX with or without cetuximab in patients with stage III CC. For each of these two classification systems, a predictor tool was developed and adapted to FFPE samples. The NanoString nCounter platform was used to screen 196 genes. Predictors were built from 249 frozen tumor samples previously used to build our classification system and 61 new paired FFPE/frozen samples. Both predictors were then applied to 1781 PETACC-8 FFPE samples. Subtypes associations to clinical and molecular features were analyzed. Results: The CMS predictor assigned 297 samples to CMS1 (17%), 585 to CMS2 (34%), 68 to CMS3 (4%) and 770 to CMS4 (45%). CMS were significantly associated with several molecular and clinical features, including MSI status (49% in CMS1, p < 0.001), CIMP status (47% in CMS1, p < 0.001), KRAS mutation (75% in CMS3, p < 0.001), BRAF mutation (34% in CMS1, p < 0.001), tumor location (less proximal tumors in CMS2, p < 0.001), validating the predictor tool developed. The classification was significantly associated to prognosis in multivariate analysis, CMS4 subtype having a shorter overall survival (hazard ratio = 1.7, p= 0.021). A deleterious effect of cetuximab was observed in CMS1 (p < 0.05). Similar results were obtained with the CCMST classification. Conclusions: We validated molecular CC subtyping predictors for both CMS and CCMST classifications on PETACC-8 FFPE samples. The prognostic value of CMS and CCMST classifications was confirmed, stem-like tumors being associated with a poor prognosis. These results pave the avenue for widely use of the CC molecular classification in clinical routine.

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Li, Hu, Peng Zou, Wei Hong Han, and Rong Ze Xia. "Imbalanced Data Classification Based on Clustering." Applied Mechanics and Materials 443 (October 2013): 741–45. http://dx.doi.org/10.4028/www.scientific.net/amm.443.741.

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Many real world data is imbalanced, i.e. one category contains significantly more samples than other categories. Traditional classification methods take different categories equally and are often ineffective. Based on the comprehensive analysis of existing researches, we propose a new imbalanced data classification method based on clustering. The method clusters both majority class and minority class at first. Then, clustered minority class will be over-sampled by SMOTE while clustered majority class be under-sampled randomly. Through clustering, the proposed method can avoid the loss of useful information while resampling. Experiments on several UCI datasets show that the proposed method can effectively improve the classification results on imbalanced data.

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Luo, Dan, and Xili Wang. "A Large Size Image Classification Method Based on Semi-supervised Learning." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 5 (September 22, 2020): 669–80. http://dx.doi.org/10.2174/1874476105666190830110150.

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Background: Semi-supervised learning in the machine learning community has received widespread attention. Semi-supervised learning can use a small number of tagged samples and a large number of untagged samples for efficient learning. Methods: In 2014, Kim proposed a new semi-supervised learning method: the minimax label propagation (MMLP) method. This method reduces time complexity to O (n), with a smaller computation cost and stronger classification ability than traditional methods. However, classification results are not accurate in large-scale image classifications. Thus, in this paper, we propose a semisupervised image classification method, which is an MMLP-based algorithm. The main idea is threefold: (1) Improving connectivity of image pixels by pixel sampling to reduce the image size, at the same time, reduce the diversity of image characteristics; (2) Using a recall feature to improve the MMLP algorithm; (3) through classification mapping, gaining the classification of the original data from the classification of the data reduction. Results: In the end, our algorithm also gains a minimax path from untagged samples to tagged samples. The experimental results proved that this algorithm is applicable to semi-supervised learning on small-size and that it can also gain better classification results for large-size image at the same time. Conclusion: In our paper, considering the connectivity of the neighboring matrix and the diversity of the characteristics, we used meanshift clustering algorithm, next we will use fuzzy energy clustering on our algorithm. We will study the function of these paths.

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Zhou, Dongning, Lu Lu, Junhong Zhao, Dali Wang, Wenlian Lu, and Jie Yang. "A new learning algorithm based on strengthening boundary samples for convolutional neural networks." MATEC Web of Conferences 327 (2020): 02004. http://dx.doi.org/10.1051/matecconf/202032702004.

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CNN is an artificial neural network that can automatically extract features with relatively few parameters, which is the advantage of CNN in image classification tasks. The purpose of this paper is to propose a new algorithm to improve the classification performance of CNN by strengthening boundary samples. The samples with predicted values near the classification boundary are recorded as hard samples. In this algorithm, the errors of hard samples are added as a penalty term of the original loss function. Multi-classification and binary classification experiments were performed using the MNIST data set and three sub-data sets of CIFAR-10, respectively. The experimental results prove that the accuracy of the new algorithm is improved in both binary classification and multi-classification problems.

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Ulfenborg, Benjamin, Karin Klinga-Levan, and Björn Olsson. "Classification of Tumor Samples from Expression Data Using Decision Trunks." Cancer Informatics 12 (January 2013): CIN.S10356. http://dx.doi.org/10.4137/cin.s10356.

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We present a novel machine learning approach for the classification of cancer samples using expression data. We refer to the method as “decision trunks,” since it is loosely based on decision trees, but contains several modifications designed to achieve an algorithm that: (1) produces smaller and more easily interpretable classifiers than decision trees; (2) is more robust in varying application scenarios; and (3) achieves higher classification accuracy. The decision trunk algorithm has been implemented and tested on 26 classification tasks, covering a wide range of cancer forms, experimental methods, and classification scenarios. This comprehensive evaluation indicates that the proposed algorithm performs at least as well as the current state of the art algorithms in terms of accuracy, while producing classifiers that include on average only 2–3 markers. We suggest that the resulting decision trunks have clear advantages over other classifiers due to their transparency, interpretability, and their correspondence with human decision-making and clinical testing practices.

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Teng, Xin Peng, Shun Lin Song, and Yong Zhao Zhan. "Statistical Class Feature in Texture Analysis of Remote Sensing Imagery." Advanced Materials Research 518-523 (May 2012): 5749–53. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.5749.

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This paper we selected 5 typical texture class samples from Quick Bird RGB fused data with 0.61m resolution. We used GLCMs to quantitatively calculate texture features, which parameter values are suitable for the specific texture classifications. Six statistical features for every class sample in four orientations and 1 pixel of pair-wise distance were obtained, including: energy, entropy, contrast, homogeneity, correlation, and dissimilarity respectively. The average values in four directions were computed and compared. The results show that dissimilarity and entropy have biggest value differences among six samples. They are the most important features for classification or recognition of class samples. The statistics of dissimilarity, entropy, homogeneity, contrast have been demonstrated a decrease in classification ability. The results of the research supplied important references for the quantitative interpretation of VHR Quick Bird imagery in the applications of land cover/use classification and mapping.

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Zhang, Xiangrong, Licheng Jiao, Anand Paul, Yongfu Yuan, Zhengli Wei, and Qiang Song. "Semisupervised Particle Swarm Optimization for Classification." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/832135.

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A semisupervised classification method based on particle swarm optimization (PSO) is proposed. The semisupervised PSO simultaneously uses limited labeled samples and large amounts of unlabeled samples to find a collection of prototypes (or centroids) that are considered to precisely represent the patterns of the whole data, and then, in principle of the “nearest neighborhood,” the unlabeled data can be classified with the obtained prototypes. In order to validate the performance of the proposed method, we compare the classification accuracy of PSO classifier, k-nearest neighbor algorithm, and support vector machine on six UCI datasets, four typical artificial datasets, and the USPS handwritten dataset. Experimental results demonstrate that the proposed method has good performance even with very limited labeled samples due to the usage of both discriminant information provided by labeled samples and the structure information provided by unlabeled samples.

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Moraes, D., P. Benevides, F. D. Moreira, H. Costa, and M. Caetano. "EXPLORING THE USE OF CLASSIFICATION UNCERTAINTY TO IMPROVE CLASSIFICATION ACCURACY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 81–86. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-81-2021.

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Abstract. Supervised classification of remotely sensed images has been widely used to map land cover and land use. Since the performance of supervised methods depends on the quality of the training data, it is essential to develop methods to generate an enhanced training dataset. Active learning represents an alternative for such purpose as it proposes to create a dataset of optimized samples, normally collected based on classification uncertainty. However, it is heavily dependent on human interaction, since the user has to label selected samples over a number of iterations. In this paper, we explore the use of uncertainty to improve classification accuracy through a single iteration. We conducted experiments in a region of Portugal (Trás-os-Montes), using multi-temporal Sentinel-2 images. The proposed approach consisted in computing the classification uncertainty of a Random Forest to collect additional training data from areas of high uncertainty and perform a new classification. An accuracy assessment was performed to compare the overall accuracy of the initial and new classifications. The results exhibited an increase in accuracy, though considered not statistically significant. Obstacles related to labelling additional sampling units resulted in a lack of additional training data for various classes, which might have limited the accuracy improvement. Additionally, an uneven proportion of additional training sampling units per class and the collection of new sample data from a limited number of uncertainty regions might also have prevented a higher increase in accuracy. Nevertheless, visual inspection of the maps revealed that the new classification reduced the confusion between some classes.

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TRAN, Dang Hung, Tu Bao HO, Tho Hoan PHAM, and Kenji SATOU. "MicroRNA Expression Profiles for Classification and Analysis of Tumor Samples." IEICE Transactions on Information and Systems E94-D, no. 3 (2011): 416–22. http://dx.doi.org/10.1587/transinf.e94.d.416.

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Deng, Fei, Shengliang Pu, Xuehong Chen, Yusheng Shi, Ting Yuan, and Shengyan Pu. "Hyperspectral Image Classification with Capsule Network Using Limited Training Samples." Sensors 18, no. 9 (September 18, 2018): 3153. http://dx.doi.org/10.3390/s18093153.

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Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs).

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Liu, Wei, Zhiming Luo, and Shaozi Li. "Improving deep ensemble vehicle classification by using selected adversarial samples." Knowledge-Based Systems 160 (November 2018): 167–75. http://dx.doi.org/10.1016/j.knosys.2018.06.035.

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Kharin, Yu S., and A. G. Medvedev. "Estimate of the stability of statistical classification for dependent samples." Journal of Mathematical Sciences 69, no. 4 (April 1994): 1238–42. http://dx.doi.org/10.1007/bf01249812.

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Quirino, José R., Osvaldo Resende, Natalia N. Fonseca, Daniel E. C. de Oliveira, Fatima C. Parizzi, and Tiago A. de Souza. "Size of samples and homogenizers during classification of damaged soybeans." Revista Brasileira de Engenharia Agrícola e Ambiental 23, no. 5 (May 2019): 378–82. http://dx.doi.org/10.1590/1807-1929/agriambi.v23n5p378-382.

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ABSTRACT Grain quality determination involves important stages such as collection of the representative sample, homogenization, and dilution. The interrelation among sampling, homogenization, and working sample size is essential to the reliability of the information generated. Therefore, this work aimed to analyse the performance of mechanical homogenizers used in the commercialization of grains in Brazil, as a function of the size of the working sample masses during grain classification. The samples were homogenized and diluted in Boerner, 16:1 multichannel splitter, and 4:1 multichannel splitter until reaching masses of 0.025, 0.050, 0.075, 0.100 and 0.125 kg to determine the level of damaged grains. A 3 x 4 x 5 factorial design was used, meaning three treatments relative to homogenizers (Boerner, 16:1 multichannel splitter, and 4:1 multichannel splitter), four dilutions (4, 8, 12 and 16% damaged grains), and five grain sample sizes (0.025, 0.050, 0.075, 0.100 and 0.125 kg) with nine repetitions. The means were compared by Tukey test and to the original means of prepared samples (4, 8, 12, and 16%) by Student’s t-test. Working samples can be utilized with masses between 0.025 and 0.125 kg to classify damaged soybeans grains. The devices Boerner, 16:1 multichannel splitter, and 4:1 multichannel splitter are similar in the reduction and homogenization of soybean samples for different levels of damaged grains and sample sizes.

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Journal articles on the topic 'Classification of samples'

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