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Stepwise classification of cancer samples using clinical and molecular data

BMC BIOINFORMATICS 2011年第1期12卷 1-12页

作者： Obulkasim, Askar Meijer, Gerrit A. van de Wiel, Mark A. Vrije Univ Amsterdam Med Ctr Dept Epidemiol & Biostat Amsterdam Netherlands Vrije Univ Amsterdam Med Ctr Dept Pathol Amsterdam Netherlands Vrije Univ Amsterdam Dept Math Amsterdam Netherlands

Background: Combining clinical and molecular data types may potentially improve prediction accuracy of a classifier. However, currently there is a shortage of effective and efficient statistical and bioinformatic tools for true integrative data analysis. Existing integrative classifiers have two main disadvantages: First, coarse combination may lead to subtle contributions of one data type to be overshadowed by more obvious contributions of the other. Second, the need to measure both data types for all patients may be both unpractical and (cost) inefficient. Results: We introduce a novel classification method, a stepwise classifier, which takes advantage of the distinct classification power of clinical data and high-dimensional molecular data. We apply classification algorithms to two data types independently, starting with the traditional clinical risk factors. We only turn to relatively expensive molecular data when the uncertainty of prediction result from clinical data exceeds a predefined limit. Experimental results show that our approach is adaptive: the proportion of samples that needs to be re-classified using molecular data depends on how much we expect the predictive accuracy to increase when re-classifying those samples. Conclusions: Our method renders a more cost-efficient classifier that is at least as good, and sometimes better, than one based on clinical or molecular data alone. Hence our approach is not just a classifier that minimizes a particular loss function. Instead, it aims to be cost-efficient by avoiding molecular tests for a potentially large subgroup of individuals;moreover, for these individuals a test result would be quickly available, which may lead to reduced waiting times (for diagnosis) and hence lower the patients distress. Stepwise classification is implemented in R-package stepwiseCM and available at the Bioconductor website.

关键词： Partial Little Square Data Type Random Forest Molecular Data classification algorithm

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Methods for pattern selection, class-specific feature selection and classification for automated learning

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NEURAL NETWORKS 2013年 41卷 113-129页

作者： Roy, Asim Mackin, Patrick D. Mukhopadhyay, Somnath Arizona State Univ Dept Informat Syst Tempe AZ 85287 USA Black Hills State Univ Spearfish SD 57799 USA Univ Texas El Paso Dept Informat & Decis Sci El Paso TX 79968 USA

This paper presents methods for training pattern (prototype) selection, class-specific feature selection and classification for automated learning. For training pattern selection, we propose a method of sampling that extracts a small number of representative training patterns (prototypes) from the dataset. The idea is to extract a set of prototype training patterns that represents each class region in a classification problem. In class-specific feature selection, we try to find a separate feature set for each class such that it is the best one to separate that class from the other classes. We then build a separate classifier for that class based on its own feature set. The paper also presents a new hypersphere classification algorithm. Hypersphere nets are similar to radial basis function (RBF) nets and belong to the group of kernel function nets. Polynomial time complexity of the methods is proven. Polynomial time complexity of learning algorithms is important to the field of neural networks. Computational results are provided for a number of well-known datasets. None of the parameters of the algorithm were fine tuned for any of the problems solved and this supports the idea of automation of learning methods. Automation of learning is crucial to wider deployment of learning technologies. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Hypersphere net classification algorithm Feature selection Complexity of learning Polynomial time complexity Training pattern selection Automated learning

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Neuro-computing vector classification SVM schemes to integrate the overmodulation region in neutral point clamped, (NPC) converters

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2007年第3期22卷 995-1004页

作者： Saeedifard, Maryarn Bakhshai, Alireza Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Queens Univ Dept Elect & Comp Engn Kingston ON K7L 3N6 Canada

Three-level neutral point clamped (NPC) voltage source converters have recently emerged as important alternatives to conventional two-level converter topologies in high-power medium-voltage energy conversion applications, particularly in high performance ac motor drive systems. An all-inclusive modulation strategy for NPC converters should have the capability of extending the operating range of the converter into the overmodulation region with a smooth and linear transition characteristic. An overmodulation switching strategy based on the space vector classification technique for three-level NPC converters is introduced in this paper. The proposed overmodulation modes, make possible continuous control of the output voltage up to the maximum possible with a smooth linear transition characteristic, and minimum distortions. A theoretical basis for the vector classification space vector modulation technique in the overmodulation region is presented, and the proposed overmodulation schemes are validated by analysis, simulation and experimentation on a 2-KVA three-level NPC converter laboratory prototype.

关键词： classification algorithm overmodulation space vector modulation (SVM) three-level converter

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Multimodal Sparse Representation-Based classification Scheme for RF Fingerprinting

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IEEE COMMUNICATIONS LETTERS 2019年第5期23卷 867-870页

作者： Yang, Kiwon Kang, Jusung Jang, Jehyuk Lee, Heung-No Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju 61005 South Korea

In this letter, we propose a multimodal method for improving radio frequency (RF) fingerprinting performance that uses multiple features cultivated from RF signals. Combining multiple features, including a falling transient feature that has not previously been used in RF fingerprinting studies, we aim to demonstrate that the proposed method results in improved accuracy. We show that a sparse representation-based classification (SRC) scheme can be a good platform for combining multiple features. The experimental results on RF signals acquired from eight walkie-talkies show that the RF fingerprinting accuracy of the proposed method improves significantly as the number of features increases.

关键词： classification algorithm feature extraction multimodality RF fingerprinting radio frequency identification

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Hyperspectral Identification and classification of Oilseed Rape Waterlogging Stress Levels Using Parallel Computing

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IEEE ACCESS 2018年 6卷 57663-57675页

作者： Xia, Ji'an Yang, Yuwang Cao, Hongxin Zhang, Weixin Xu, Lei Wan, Qian Ke, Yaqi Zhang, Wenyu Ge, Daokuo Huang, Bo Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Jiangsu Acad Agr Sci Inst Agr Informat Nanjing 210014 Jiangsu Peoples R China

This paper aims to evaluate the feasibility and performance of two related applications in agricultural big data: hyperspectral imaging and parallel computing. After selecting two oilseed rape varieties (NingYou 22 and NingZa 19) as the objects of study, we captured hyperspectral images of these siliques following their exposure to three different waterlogging stress levels (0, 3, and 6 days). The machine learning library for Spark was used to realize both artificial neural network (ANN) and support vector machine (SVM) classification algorithms, and to conduct hyperspectral classification analysis of the oilseed rape siliques under the various levels of waterlogging stress on the parallel computing platform. From the classification data sets, 70% of the data were randomly selected for training, and the remaining 30% were used for prediction. The experimental results indicate that, when the hyperspectral image of a region of interest (400-1000 nm) was extracted and combined with the spectrum image data, the oilseed rape waterlogging detection model based on the Spark parallel computing framework was feasible and efficient. For the multi-class classification problem, the accuracy of the ANN algorithm was superior to that of the SVM, but its convergence time exceeded that of the SVM algorithm. Using the ANN and SVM algorithms for binary classification of the samples from both varieties, the results indicate that the performance of the SVM algorithm was superior in terms of the binary classification problem. Meanwhile, of the two oilseed rape varieties, the NZ 19 waterlogging samples yielded better classification results. Five optimal wavebands (512, 621, 689, 953, and 961 nm) were selected as the inputs for the classification algorithm. The results show that the classification accuracy of full-waveband hyperspectral imaging was slightly higher than that of optimal waveband imaging, while the ANN algorithm was more accurate than the SVM algorithm. Finally, the three in

关键词： Rapeseed silique hyperspectral image parallel computing machine learning spark classification algorithm

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A Measurement System to Monitor Postural Behavior: Strategy Assessment and classification Rating

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2020年第10期69卷 8020-8031页

作者： Ando, Bruno Marletta, Vincenzo Baglio, Salvatore Crispino, Ruben Mostile, Giovanni Dibilio, Valeria Nicoletti, Alessandra Zappia, Mario Univ Catania Dept Elect Elect & Informat Engn DIEEI I-95125 Catania Italy AOU Policlin Vittorio Emanuele Clin Neurol I-95125 Catania Italy

Postural instability is a symptom of disorders like ear diseases, high blood pressure, diabetes, psychiatric disorders, and neurodegenerative diseases like Parkinson and one of the main causes of falls. A continuous monitoring of the patient's postural stability while performing the daily activities would be of strategic interest for the doctors to gain continuous information on the user pathology or its progression. This article focuses on the experimental assessment of a wearable inertial device aimed at the continuous monitoring of the user's postural sway, which exploits a novel postural sway classification algorithm providing a classification index. Two performance keys have been proposed: 1) to assess the postural sway monitoring strategy and 2) to rate the reliability of the classification outcome. Experiments reported in this article have been performed involving "healthy" users miming typical instability dynamics under the supervision of the neurologists. In the case of study addressed through this article, a full classification success has been obtained with an overall reliability of about 70%.

关键词： Monitoring Biomedical monitoring Aging Reliability Neurological diseases classification algorithm continuous monitoring inertial sensors neurological diseases postural instability reliability index (RI) stabilogram

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An Approach of Genetic Programming for Music Emotion classification

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2013年第6期11卷 1290-1299页

作者： Bang, Sung-Woo Kim, Jaekwang Lee, Jee-Hyong Sungkyunkwan Univ Coll Informat & Commun Engn Suwon South Korea

In this paper, we suggest a new approach of genetic programming for music emotion classification. Our approach is based on Thayer's arousal-valence plane which is one of representative human emotion models. Thayer's plane which says human emotions is determined by the psychological arousal and valence. We map music pieces onto the arousal-valence plane, and classify the music emotion in that space. We extract 85 acoustic features from music signals, rank those by the information gain and choose the top k best features in the feature selection process. In order to map music pieces in the feature space onto the arousal-valence space, we apply genetic programming. The genetic programming is designed for finding an optimal formula which maps given music pieces to the arousal-valence space so that music emotions are effectively classified. k-NN and SVM methods which are widely used in classification are used for the classification of music emotions-in the arousal-valence space. For verifying our method, we compare with other six existing methods on the same music data set. With this experiment, we confirm the proposed method is superior to others.

关键词： classification algorithm emotion recognition genetic programming music information retrieval

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Automatic classification of the Vestibulo-Ocular Reflex Nystagmus: Integration of Data Clustering and System Identification

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2016年第4期63卷 850-858页

作者： Ranjbaran, Mina Smith, Heather L. H. Galiana, Henrietta L. McGill Univ Dept Biomed Engn 3775 Univ Montreal PQ H3A 2B4 Canada

The vestibulo-ocular reflex (VOR) plays an important role in our daily activities by enabling us to fixate on objects during head movements. Modeling and identification of the VOR improves our insight into the system behavior and improves diagnosis of various disorders. However, the switching nature of eye movements (nystagmus), including the VOR, makes dynamic analysis challenging. The first step in such analysis is to segment data into its subsystem responses (here slow and fast segment intervals). Misclassification of segments results in biased analysis of the system of interest. Here, we develop a novel three-step algorithm to classify the VOR data into slow and fast intervals automatically. The proposed algorithm is initialized using a K-means clustering method. The initial classification is then refined using system identification approaches and prediction error statistics. The performance of the algorithm is evaluated on simulated and experimental data. It is shown that the new algorithm performance is much improved over the previous methods, in terms of higher specificity.

关键词： classification algorithm data clustering nystagmus system identification vestibulo-ocular reflex

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A Comparison of Multi-Label Text classification Models in Research Articles Labeled With Sustainable Development Goals

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IEEE ACCESS 2022年 10卷 123534-123548页

作者： Carlos Morales-Hernandez, Roberto Gutierrez Jaguey, Joaquin Becerra-Alonso, David Ctr Invest Biol Noroeste La Paz 23096 Mexico Univ Loyola Andalucia Dept Quantitat Methods Seville 41704 Spain

The classification of scientific articles aligned to Sustainable Development Goals is crucial for research institutions and universities when assessing their influence in these areas. Machine learning enables the implementation of massive text data classification tasks. The objective of this study is to apply Natural Language Processing techniques to articles from peer-reviewed journals to facilitate their classification according to the 17 Sustainable Development Goals of the 2030 Agenda. This article compares the performance of multi-label text classification models based on a proposed framework with datasets of different characteristics. The results show that the combination of Label Powerset (a transformation method) with Support Vector Machine (a classification algorithm) can achieve an accuracy of up to 87% for an imbalanced dataset, 83% for a dataset with the same number of instances per label, and even 91% for a multiclass dataset.

关键词： classification algorithm multi-label text classification problem transformation method scientific articles sustainable development goals text classification

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Detection and classification of Lamination Faults in a 15 kVA Three-Phase Transformer Core Using SVM, KNN and DT algorithms

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IEEE ACCESS 2022年 10卷 50925-50932页

作者： Altayef, Ehsan Anayi, Fateh Packianather, M. Benmahamed, Youcef Kherif, Omar Cardiff Univ Wolfson Ctr Magnet Sch Engn Cardiff CF24 3AA Wales Ecole Natl Polytech ENP Lab Rech Electrotech LRE Algiers 16200 Algeria Cardiff Univ Adv High Voltage Engn Res Ctr Sch Engn Cardiff CF24 3AA Wales

This paper deals with the detection and classification of two types of lamination faults (i.e., edge burr and lamination insulation faults) in a three-phase transformer core. Previous experimental results are exploited, which are obtained by employing a 15 kVA transformer under healthy and faulty conditions. Different test conditions were considered such as the flux density, number of the affected laminations, and fault location. Indeed, the current signals were used where four features (Average, Fundamental, Total Harmonic Distortion (THD), and Standard Deviation (STD)) were extracted. Elaborating A total of 328 samples, these features are utilized as input vectors to train and test classification models based on SVM, KNN, and DT algorithms. Based on the selected features, the results confirmed that the transformer current can be used for the detection of lamination faults. An accuracy rate of more than 84% was obtained using three different classifiers. Such findings provided a promising step toward fault detection and classification in electrical transformers, helping to prevent the system and avoid other related issues such as the increase in power loss and temperature.

关键词： Power transformers Power transformer insulation Lamination Feature extraction Transformer cores Insulation Circuit faults Edge burrs classification algorithm decision tree algorithm fault detection lamination insulation KNN classifier SVM classifier transformer core

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