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Applying classification algorithms in practice

STATISTICS AND COMPUTING 1997年第1期7卷 45-56页

作者： Brodley, CE Smyth, P PURDUE UNIV SCH ELECT & COMP ENGN W LAFAYETTE IN 47906 USA UNIV CALIF IRVINE IRVINE CA 92717 USA

In this paper we present a perspective on the overall process of developing classifiers for real-world classification problems. Specifically, we identify, categorize and discuss the various problem-specific factors that influence the development process. Illustrative examples are provided to demonstrate the iterative nature of the process of applying classification algorithms in practice. In addition, we present a case study of a large scale classification application using the process framework described, providing an end-to-end example of the iterative nature of the application process. The paper concludes that the process of developing classification applications for operational use involves many factors not normally considered in the typical discussion of classification models and algorithms.

关键词： classification algorithms diagnostics model selection

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Evaluation of the performance of classification algorithms for XFEL single-particle imaging data

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IUCRJ 2019年第Pt 2期6卷 331-340页

作者： Shi, Yingchen Yin, Ke Tai, Xuecheng DeMirci, Hasan Hosseinizadeh, Ahmad Hogue, Brenda G. Li, Haoyuan Ourmazd, Abbas Schwander, Peter Vartanyants, Ivan A. Yoon, Chun Hong Aquila, Andrew Liu, Haiguang Tsinghua Univ Dept Engn Phys 30 Shuangqing Rd Beijing 100084 Peoples R China Beijing Computat Sci Res Ctr Complex Syst Div 8 E Xibeiwang Rd Beijing 100193 Peoples R China Huazhong Univ Sci & Technol Ctr Math Sci Wuhan 430074 Hubei Peoples R China Univ Bergen Dept Math POB 7800 N-5020 Bergen Norway SLAC Natl Accelerator Lab Biosci Div 2575 Sand Hill Rd Menlo Pk CA 94025 USA SLAC Natl Accelerator Lab Stanford PULSE Inst 2575 Sand Hill Rd Menlo Pk CA 94025 USA Univ Wisconsin Dept Phys Milwaukee WI 53201 USA Arizona State Univ Biodesign Inst Biodesign Ctr Immunotherapy Vaccines & Virotherap Tempe AZ 85287 USA SLAC Natl Accelerator Lab Linac Coherent Light Source 2575 Sand Hill Rd Menlo Pk CA 94025 USA Stanford Univ Dept Phys 450 Serra Mall Stanford CA 94305 USA Deutsch Elektronen Synchrotron DESY Notkestr 85 D-22607 Hamburg Germany Natl Res Nucl Univ MEPhI Moscow Engn Phys Inst Kashirskoe Shosse 31 Moscow 115409 Russia

Using X-ray free-electron lasers (XFELs), it is possible to determine three-dimensional structures of nanoscale particles using single-particle imaging methods. classification algorithms are needed to sort out the single-particle diffraction patterns from the large amount of XFEL experimental data. However, different methods often yield inconsistent results. This study compared the performance of three classification algorithms: convolutional neural network, graph cut and diffusion map manifold embedding methods. The identified single-particle diffraction data of the PR772 virus particles were assembled in the three-dimensional Fourier space for real-space model reconstruction. The comparison showed that these three classification methods lead to different datasets and subsequently result in different electron density maps of the reconstructed models. Interestingly, the common dataset selected by these three methods improved the quality of the merged diffraction volume, as well as the resolutions of the reconstructed maps.

关键词： X-ray free-electron lasers (XFELs) single-particle imaging classification algorithms electron-density map reconstruction

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DEVELOPMENT AND EVALUATION OF SPECTRAL classification algorithms FOR FLUORESCENCE GUIDED LASER ANGIOPLASTY

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 1989年第4期36卷 424-431页

作者： OBRIEN, KM GMITRO, AF GINDI, GR STETZ, ML CUTRUZZOLA, FW LAIFER, LI DECKELBAUM, LI YALE UNIV SCH MEDDEPT RADIOLNEW HAVENCT 06510 VET ADM MED CTR W HAVENCT 06516 UNIV ARIZONA ARIZONA HLTH SCI CTRDEPT RADIOLTUCSONAZ 85724

The feasibility of utilizing spectral information to discriminate arterial tissue type is considered. Arterial fluorescence spectra from 350 to 700 nm were obtained from 100 human aortic specimens. Seven spectral classification algorithms were developed with the following techniques: multivariate linear regression, stepwise multivariate linear regression, principal components analysis, decision plane analysis, Bayes decision theory, principal peak ratio, and spectral width. The classification ability of each algorithm was evaluated by its application to the training set and to a validation set containing 82 additional spectra. All seven spectral classification algorithms prospectively classified atherosclerotic and normal aortas with an accuracy greater than 80% (range: 82-96%). Laser angioplasty systems incorporating spectral classification algorithms may therefore be capable of detection and selective ablation of atherosclerotic plaque.< >

关键词： classification algorithms Fluorescence Angioplasty Linear regression Humans Principal component analysis Algorithm design and analysis Decision theory Laser ablation Laser theory

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classification algorithms applied to blood-based transcriptome meta-analysis to predict idiopathic Parkinson's disease

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COMPUTERS IN BIOLOGY AND MEDICINE 2020年 124卷 103925-103925页

作者： Falchetti, Marcelo Prediger, Rui Daniel Zanotto-Filho, Alfeu Univ Fed Santa Catarina UFSC Dept Farmacol Lab Expt Doencas Neurodegenerat Florianopolis SC Brazil Univ Fed Santa Catarina UFSC Dept Farmacol Lab Farmacol Bioquim & Mol Florianopolis SC Brazil Univ Fed Santa Catarina UFSC Dept Farmacol Ctr Ciencias Biol CCB Bloco DCampus Trindade BR-88049900 Florianopolis SC Brazil

Diagnosis of Parkinson's disease (PD) remains a challenge in clinical practice, mostly due to lack of peripheral blood markers. Transcriptomic analysis of blood samples has emerged as a potential means to identify bio-markers and gene signatures of PD. In this context, classification algorithms can assist in detecting data patterns such as phenotypes and transcriptional signatures with potential diagnostic application. In this study, we per-formed gene expression meta-analysis of blood transcriptome from PD and control patients in order to identify a gene-set capable of predicting PD using classification algorithms. We examined microarray data from public repositories and, after systematic review, 4 independent cohorts (GSE6613, GSE57475, GSE72267 and GSE99039) comprising 711 samples (388 idiopathic PD and 323 healthy individuals) were selected. Initially, analysis of differentially expressed genes resulted in minimal overlap among datasets. To circumvent this, we carried out meta-analysis of 17,712 genes across datasets, and calculated weighted mean Hedges' g effect sizes. From the top -100-positive and negative gene effect sizes, algorithms of collinearity recognition and recursive feature elimination were used to generate a 59-gene signature of idiopathic PD. This signature was evaluated by 9 classification algorithms and 4 sample size-adjusted training groups to create 36 models. Of these, 33 showed accuracy higher than the non-information rate, and 2 models built on Support Vector Machine Regression bestowed best accuracy to predict PD and healthy control samples. In summary, the gene meta-analysis followed by machine learning methodology employed herein identified a gene-set capable of accurately predicting idio-pathic PD in blood samples.

关键词： Effect size Microarray classification algorithms Molecular signature Blood biopsy

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classification algorithms in Automatic Diagnosis of ECG Arrhythmias: A Review

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IEEE ACCESS 2024年 12卷 191921-191935页

作者： Sun, Haibo Luo, Dan Niu, Xin Zeng, Xiaoxi Zheng, Bin Liu, Hao Pan, Jingye Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China Tiangong Univ Inst Smart Wearable Elect Text Tianjin 300387 Peoples R China Tiangong Univ Shaoxing Keqiao Inst Shaoxing 312030 Zhejiang Peoples R China Tiangong Univ Sch Text Sci & Engn Tianjin 300387 Peoples R China Sichuan Univ West China Hosp Biomed Big Data Ctr Chengdu 610065 Sichuan Peoples R China Tianjin Univ Wenzhou Safety Emergency Inst Wenzhou 325000 Zhejiang Peoples R China Tianjin Univ Acad Med Engn & Translat Med Tianjin Key Lab Brain Sci & Neural Engn Tianjin 300072 Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Wenzhou 325000 Zhejiang Peoples R China Key Lab Intelligent Treatment & Life Support Crit Wenzhou 325000 Zhejiang Peoples R China

ECG (Electrocardiogram), the most commonly used tool in the diagnosis of cardiac diseases, contains a large amount of physiologic information about the electrical activity of the heart. Research on automatic diagnosis of cardiac diseases by means of computer-aided ECG diagnosis has been carried out for decades. Computer assisted therapy is able to timely detect heart diseases and reduce the mortality rate of cardiovascular disease patients. In recent years, classification algorithms applied to automatic ECG arrhythmia diagnosis have been proposed and optimized. With the application and development of neural network-based deep learning technology in automatic ECG diagnosis, the accuracy and reliability of automatic ECG arrhythmia classification have been significantly improved. This paper systematically analyzes the literatures related to the automatic classification of arrhythmias based on ECG signals, summarizes the classification algorithms of different arrhythmias based on neural network model and non-neural network model, and summarizes the classification characteristics of the two main classification models in the application of automatic diagnosis of arrhythmias under the same AAMI standard and MIT-BIH arrhythmia database. This paper summarizes the selected research results in the field of arrhythmia classification under the same database from the perspective of data quality, evaluation paradigm and performance indicators, and discusses the correlation between the specific characteristics of arrhythmia detection and different classification algorithms. Finally, the existing problems in the research of automatic classification of arrhythmias are put forward, which provides a reference for the future development and clinical practice of automatic diagnosis of arrhythmias.

关键词： Electrocardiography Arrhythmia Databases classification algorithms Feature extraction Neural networks Brain modeling Deep learning Medical diagnostic imaging Hospitals Arrhythmias automatic diagnosis ECG signal neural networks support vector machine

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Metrics for Evaluating classification algorithms 1st

Metrics for Evaluating Classification Algorithms

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21st International Conference on Informatics in Economy (IE) - Education, Research and Business Technologies

作者： Muntean, Mihaela Militaru, Florin-Daniel West Univ Timisoara Timisoara Romania

ISBN: (纸本)9789811967573;9789811967559;9789811967542

One of the most important topics in machine learning is how to evaluate the models, which means measuring how accurately they predict the expected outcome. In addition to accuracy, there are a number of other metrics for evaluating classifier performance. Model evaluation is introduced in a prediction framework that is implemented using automated machine learning. The performance metrics are calculated for each classification model generated for our analysis. Unlabeled data gathered using a 360-degree evaluation form goes through a clustering process before being analyzed by classification. The most performant classifier is identified through model evaluation, and a detailed metrics analysis is performed.

关键词： CatBoost classifier classification accuracy classification algorithms

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New classification algorithms for Developing Online Program Recommendation Systems

New Classification Algorithms for Developing Online Program ...

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International Conference on Mobile, Hybrid and On-Line Learning

作者： Meller, Thomas Wang, Eric Lin, Fuhua Yang, Chunsheng Ctr Educ & Informat Technol New Westminster BC Canada Athabasca Univ Sch Business Athabasca AB Canada Athabasca Univ Sch Comp & Informat Sci Athabasca AB Canada Natl Res Council Canada Inst Informat Technol Ottawa ON Canada

ISBN: (纸本)9781424433612

This paper presents two novel nearest-neighbor-like classification algorithms for program recommendation in a Web-based system, which provides a program planning service to academic advisors and students of post-secondary institutions. To evaluate the accuracy of classification for program recommendations generated by our algorithm, a statistical study was conducted through comparing our algorithm against two well-known classification algorithms, the Naive Bayes algorithm and the J48 algorithm, for making recommendations to students based on their academic history. The study shows that our proposed nearest-neighbor-like algorithms outperform the two well-known classification algorithms in terms of student classification success rate when there is uncertainty present in the data.

关键词： classification algorithms program planning recommendation systems data mining

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classification algorithms Research on Facial Expression Recognition

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Physics Procedia 2012年 25卷 1241-1244页

作者： Jun Ou Department of Network Engineering Hainan Software Profession InstituteQiongHai Hainan 571400 P.R.China

In order to improve the reliability of facial expression recognition system, and reduce the chance of false positives caused by error, classification strategy is important in recognition process. In the process, the k-nearest neighbor algorithm is improved. Experimental results show that the performance of the proposed method is excellent when it is applied to facial expression recognition system.

关键词： classification algorithms Facial expression recognition Feature characterization

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Mapping of Krau Wildlife Reserve (KWR) protected area using Landsat 8 and supervised classification algorithms

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Remote Sensing Applications: Society and Environment 2018年 10卷 24-35页

作者： Shaharum, Nur Shafira Nisa Shafri, Helmi Zulhaidi Mohd Gambo, Jibrin Abidin, Fauzul Azim Zainal Department of Civil Engineering Faculty of Engineering Universiti Putra Malaysia (UPM) Serdang 43400 Selangor Malaysia Geospatial Information Science Research Centre (GISRC) Faculty of Engineering Universiti Putra Malaysia (UPM) Serdang 43400 Selangor Malaysia Department of Wildlife and National Parks (DWNP) Peninsular Malaysia Km10 Jalan Cheras Kuala Lumpur 56100 Malaysia

Human-dominated ecosystems speed up the loss of habitats, populations, and species. Thus, monitoring and managing the Earth's heritage of biodiversity is a challenge in natural resource management. Mapping protected areas (PAs) is essential in understanding the disturbance that can affect biodiversity and conservation management. Land use-land cover (LULC) maps can be used as a decision making tool by policy makers to ensure sustainable development and understanding of the effect of human activities within and around PAs. However, in Malaysia, the limited updated maps of PAs make the effective management of PAs problematic. Therefore, this study aimed to produce an updated Land LULC map for the PA Krau Wildlife Reserve (KWR) and its surroundings using remote sensing and related geospatial technologies. Three supervised classification algorithms were used and compared. Multidated images from Landsat 8 were utilized, and spectral angle mapper (SAM), support vector machine (SVM), and artificial neural network (ANN) classifiers were applied and evaluated. The approaches of pan-sharpening and cloud patching were used to enhance the accuracy of LULC classification. The images were classified into five classes: dense forest, less dense forest or agriculture, built-up area, bare soil, and water. The overall accuracies of SAM, ANN, and SVM for the 15 m spatial resolution images were 81.96%, 98.22% and 97.40%, respectively. The ANN map produced the highest overall accuracy and was consequently utilized to extract additional information related to disturbance and encroachment within and around the PA. Findings indicated that socioeconomic activities played a major role in altering the environment of KWR. © 2018 Elsevier B.V.

关键词： classification algorithms Geospatial technologies Krau Wildlife Reserve LULC Protected Area

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Comparative study of classification algorithms for damage classification in smart composite laminates

Comparative study of classification algorithms for damage cl...

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Conference on Nanosensors, Biosensors, Info-Tech Sensors and 3D Systems

作者： Khan, Asif Ryoo, Chang-Kyung Kim, Heung Soo Dongguk Univ Seoul Dept Mech Robot & Energy Engn 30 Pildong Ro1 Gil Seoul 04620 South Korea Inha Univ Dept Aerosp Engn 100 Inharo Incheon 22212 South Korea

ISBN: (纸本)9781510608191;9781510608207

This paper presents a comparative study of different classification algorithms for the classification of various types of inter-ply delaminations in smart composite laminates. Improved layerwise theory is used to model delamination at different interfaces along the thickness and longitudinal directions of the smart composite laminate. The input-output data obtained through surface bonded piezoelectric sensor and actuator is analyzed by the system identification algorithm to get the system parameters. The identified parameters for the healthy and delaminated structure are supplied as input data to the classification algorithms. The classification algorithms considered in this study are ZeroR, classification via regression, Naive Bayes, Multilayer Perceptron, Sequential Minimal Optimization, Multiclass-Classifier, and Decision tree (J48). The open source software of Waikato Environment for Knowledge Analysis (WEKA) is used to evaluate the classification performance of the classifiers mentioned above via 75-25 holdout and leave-one-sample-out cross-validation regarding classification accuracy, precision, recall, kappa statistic and ROC Area.

关键词： classification algorithms smart composite laminates delamination improved layerwise theory WEKA

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