Multi-class Classification Method Research Articles

Data fusion for fault diagnosis using multi-class Support Vector Machines

Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space. Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are processed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields.

Probability Estimates for Multi-class Classification by Pairwise Coupling

Pairwise coupling is a popular multi-class classification method that combines all comparisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. Both methods can be reduced to linear systems and are easy to implement. We show conceptually and experimentally that the proposed approaches are more stable than the two existing popular methods: voting and the method by Hastie and Tibshirani (1998)

A dynamic material library for the representation of single-phase polyhedral microstructures

Quantitative estimation of features from sectional images of microstructures is fundamental to determining the microstructural influences on material behavior. Geometric data extracted from planar materialographic sections through image analysis techniques are primarily lower-order descriptors of the microstructure. Such descriptors do not contain complete morphological information and hence cannot fully describe the material microstructure. This work uses a multi-class support vector machine classification method in conjunction with principal component analysis to build a dynamic and evolving microstructure library that can be used to efficiently describe single-phase polyhedral microstructures. Lower-order descriptors are initially used to associate the material microstructure to classes of microstructures stored in a digital material library. Complete description is obtained by quantifying the microstructure using the coefficients of a continuously updating basis within a class of the library. These techniques are essential towards the development of a dynamic materials library that will be able to analyze, classify and represent microstructures for modeling, accelerating the design and testing of single-phase polycrystalline materials.

Field trial of applicability of lot quality assurance sampling survey method for rapid assessment of prevalence of active trachoma.

To test the applicability of lot quality assurance sampling (LQAS) for the rapid assessment of the prevalence of active trachoma. Prevalence of active trachoma in six communities was found by examining all children aged 2-5 years. Trial surveys were conducted in these communities. A sampling plan appropriate for classifying communities with prevalences < or =20% and > or =40% was applied to the survey data. Operating characteristic and average sample number curves were plotted, and screening test indices were calculated. The ability of LQAS to provide a three-class classification system was investigated. Ninety-six trial surveys were conducted. All communities with prevalences < or =20% and > or =40% were identified correctly. The method discriminated between communities with prevalences < or =30% and >30%, with sensitivity of 98% (95% confidence interval (CI)=88.2-99.9%), specificity of 84.4% (CI=69.9-93.0%), positive predictive value of 87.7% (CI=75.7-94.5%), negative predictive value of 97.4% (CI=84.9-99.9%), and accuracy of 91.7% (CI=83.8-96.1%). Agreement between the three prevalence classes and survey classifications was 84.4% (CI=75.2-90.7%). The time needed to complete the surveys was consistent with the need to complete a survey in one day. Lot quality assurance sampling provides a method of classifying communities according to the prevalence of active trachoma. It merits serious consideration as a replacement for the assessment of the prevalence of active trachoma with the currently used trachoma rapid assessment method. It may be extended to provide a multi-class classification method.

Reordering Adaptive Directed Acyclic Graphs for Multiclass Support Vector Machines

The problem of extending binary support vector machines (SVMs) for multiclass classification is still an ongoing research issue. Ussivakul and Kijsirikul proposed the Adaptive Directed Acyclic Graph (ADAG) approach that provides accuracy comparable to that of the standard algorithm-Max Wins and requires low computation. However, different sequences of nodes in the ADAG may provide different accuracy. In this paper we present a new method for multiclass classification, Reordering ADAG, which is the modification of the original ADAG method. We show examples to exemplify that the margin (or 2/|w| value) between two classes of each binary SVM classifier affects the accuracy of classification, and this margin indicates the magnitude of confusion between the two classes. In this paper, we propose an algorithm to choose an optimal sequence of nodes in the ADAG by considering the |w| values of all classifiers to be used in data classification. We then compare our performance with previous methods including the ADAG and the Max Wins algorithm. Experimental results demonstrate that our method gives higher accuracy. Moreover it runs faster than Max Wins, especially when the number of classes and/or the number of dimensions are relatively large.

Multi-class protein fold recognition using support vector machines and neural networks.

Protein fold recognition is an important approach to structure discovery without relying on sequence similarity. We study this approach with new multi-class classification methods and examined many issues important for a practical recognition system. Most current discriminative methods for protein fold prediction use the one-against-others method, which has the well-known 'False Positives' problem. We investigated two new methods: the unique one-against-others and the all-against-all methods. Both improve prediction accuracy by 14-110% on a dataset containing 27 SCOP folds. We used the Support Vector Machine (SVM) and the Neural Network (NN) learning methods as base classifiers. SVMs converges fast and leads to high accuracy. When scores of multiple parameter datasets are combined, majority voting reduces noise and increases recognition accuracy. We examined many issues involved with large number of classes, including dependencies of prediction accuracy on the number of folds and on the number of representatives in a fold. Overall, recognition systems achieve 56% fold prediction accuracy on a protein test dataset, where most of the proteins have below 25% sequence identity with the proteins used in training.

A characteristic matrix potential function method for multi-class pattern classification†

An algorithm based on the potential function and the characteristic matrix is presented for multi-class pattern classification. The convergence of the algorithm is proved and a numerical example is given. It is shown that the algorithm using the Kesler transformation is a special case of the characteristic matrix potential function method.

An Objective Method of Determining Grade Placement of Children's Reading Material

Previous articleNext article No AccessAn Objective Method of Determining Grade Placement of Children's Reading MaterialMabel Vogel and Carleton WashburneMabel Vogel Search for more articles by this author and Carleton Washburne Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Elementary School Journal Volume 28, Number 5Jan., 1928 Article DOIhttps://doi.org/10.1086/456072 Views: 10Total views on this site Citations: 36Citations are reported from Crossref PDF download Crossref reports the following articles citing this article:Lining Liu, Yongquan Li A new method of constructing medical Chinese readability corpus based on big data, (Jul 2022): 574–577.https://doi.org/10.1109/ICBAIE56435.2022.9985808Marina Ivanovna Solnyshkina, Danielle S. McNamara, Radif Rifkatovich Zamaletdinov Natural language processing and discourse complexity studies, Russian Journal of Linguistics 26, no.22 (Jun 2022): 317–341.https://doi.org/10.22363/2687-0088-30171Devrim DİVRİK, Dilek CERAN Mustafa Orakçı'nın “Ünlülerle Bir Gün” Serisinin Okunabilirlik Düzeyi Açısından İncelenmesi, ASYA STUDIES (Feb 2022).https://doi.org/10.31455/asya.1018747Dehong Luo, Jing Gong, Yifei Li The effect of reading comprehension questions on linguistic predictors of readability, Educational Studies 27 (Jul 2020): 1–17.https://doi.org/10.1080/03055698.2020.1798741Huiping Wang, Lijiao Yang, Huimin Xiao Construction of Quantitative Index System of Vocabulary Difficulty in Chinese Grade Reading, (Nov 2019): 480–486.https://doi.org/10.1109/IALP48816.2019.9037664Yueming Du, Lijiao Yang What affects the difficulty of Chinese syntax?, (Nov 2019): 71–74.https://doi.org/10.1109/IALP48816.2019.9037724Kathy Conklin, Richard Hyde, Fabio Parente Assessing plain and intelligible language in the Consumer Rights Act: a role for reading scores?, Legal Studies 39, no.33 (Jan 2019): 378–397.https://doi.org/10.1017/lst.2018.25Chrystalla Neofytou, Thanasis Hadzilacos A Tool for Assessing Text Suitability for Greek Language Teaching, Journal of Educational Computing Research 56, no.77 (Oct 2017): 1030–1055.https://doi.org/10.1177/0735633117731381Xiaobin Chen, Detmar Meurers Word frequency and readability: Predicting the text-level readability with a lexical-level attribute, Journal of Research in Reading 41, no.33 (Jul 2017): 486–510.https://doi.org/10.1111/1467-9817.12121James W. Cunningham, Elfrieda H. Hiebert, Heidi Anne Mesmer Investigating the validity of two widely used quantitative text tools, Reading and Writing 31, no.44 (Jan 2018): 813–833.https://doi.org/10.1007/s11145-017-9815-4S. Venkatesan Development and Validation of Graded Reading Test for Children with Learning Disabilities, Journal of Psychology 8, no.11 (Aug 2017): 11–20.https://doi.org/10.1080/09764224.2017.1305596Thomas François When readability meets computational linguistics: a new paradigm in readability, Revue française de linguistique appliquée Vol. XX, no.22 (Nov 2015): 79–97.https://doi.org/10.3917/rfla.202.0079Zhiwei Jiang, Gang Sun, Qing Gu, Lixia Yu, Daoxu Chen An Extended Graph-Based Label Propagation Method for Readability Assessment, (Nov 2015): 485–496.https://doi.org/10.1007/978-3-319-25255-1_40Irina Temnikova, Sarah Vieweg, Carlos Castillo The Case for Readability of Crisis Communications in Social Media, (Jan 2015): 1245–1250.https://doi.org/10.1145/2740908.2741718Zhiwei Jiang, Gang Sun, Qing Gu, Daoxu Chen An Ordinal Multi-class Classification Method for Readability Assessment of Chinese Documents, (Jan 2014): 61–72.https://doi.org/10.1007/978-3-319-12096-6_6Paul Deane, Thomas Quinlan, Irene Kostin AUTOMATED SCORING WITHIN A DEVELOPMENTAL, COGNITIVE MODEL OF WRITING PROFICIENCY, ETS Research Report Series 2011, no.11 (Aug 2014): i–93.https://doi.org/10.1002/j.2333-8504.2011.tb02252.xThomas François La Lisibilité Computationnelle, ITL - International Journal of Applied Linguistics 160 (Jan 2010): 75–99.https://doi.org/10.1075/itl.160.04fraC.A. Weaver, A. Renken Readability, Applied Psychology of, (Jan 2001): 12789–12791.https://doi.org/10.1016/B0-08-043076-7/01557-6Ronald A. Guillemette Predicting readability of data processing written materials, ACM SIGMIS Database: the DATABASE for Advances in Information Systems 18, no.44 (Jul 1987): 40–47.https://doi.org/10.1145/1017816.1017820CHAFFAI TEKFI READABILITY FORMULAS: AN OVERVIEW, Journal of Documentation 43, no.33 (Mar 1987): 261–273.https://doi.org/10.1108/eb026811Malcolm D. Neely, Ogden R. Lindsley Phonetic, Linguistic, and Sight Readers Produce Similar Learning with Exceptional Children, The Journal of Special Education 12, no.44 (Aug 2016): 423–441.https://doi.org/10.1177/002246697801200408Salvatore Liguori A Quantitative Assessment of the Readability of PPI's, Drug Intelligence & Clinical Pharmacy 12, no.1212 (Aug 2016): 712–716.https://doi.org/10.1177/106002807801201202Jane Blankenship The influence of mode, sub‐mode, and speaker predilection on style, Speech Monographs 41, no.22 (Jun 1974): 85–118.https://doi.org/10.1080/03637757409375826M. D. Still The Readability of Chairmen's Statements, Accounting and Business Research 3, no.99 (Dec 1972): 36–39.https://doi.org/10.1080/00014788.1972.9728995Wayne D. Lee, Bernard R. Belden A Cross-Validation Readability Study of General Psychology Textbook Material and the Dale-Chall Readability Formula, The Journal of Educational Research 59, no.88 (Dec 2014): 369–373.https://doi.org/10.1080/00220671.1966.10883383Glenn Maynard Value of twenty‐five cent books for independent recreational reading, Peabody Journal of Education 41, no.22 (Sep 1963): 86–90.https://doi.org/10.1080/01619566309537159Stewart Docter Testing the Readability of Engineering Writing, IRE Transactions on Engineering Writing and Speech 4, no.33 (Dec 1961): 91–96.https://doi.org/10.1109/TEWS.1961.4322740Emmett Albert Betts Readability: Its Application to the Elementary School, The Journal of Educational Research 42, no.66 (Dec 2014): 438–459.https://doi.org/10.1080/00220671.1949.10881708Melvin Lostutter Some Critical Factors of Newspaper Readability, Journalism Quarterly 24, no.44 (Aug 2016): 307–331.https://doi.org/10.1177/107769904702400402Irving Lorge Predicting Readability, Teachers College Record: The Voice of Scholarship in Education 45, no.66 (Apr 2022): 1–14.https://doi.org/10.1177/016146814404500604Rudolf F. Flesch Estimating the Comprehension Difficulty of Magazine Articles, The Journal of General Psychology 28, no.11 (Jan 1943): 63–80.https://doi.org/10.1080/00221309.1943.10545195Ernest Horn Language and Meaning, Teachers College Record: The Voice of Scholarship in Education 43, no.1010 (Apr 2022): 377–413.https://doi.org/10.1177/016146814204301011Edgar Dale Chapter IV: Quantitative Analysis of Documentary Materials, Review of Educational Research 9, no.55 (Jun 2016): 466–471.https://doi.org/10.3102/00346543009005466Lester R. Wheeler A Study of the Relative Difficulty of a Primary Reading Vocabulary, The Pedagogical Seminary and Journal of Genetic Psychology 52, no.11 (Mar 1938): 183–201.https://doi.org/10.1080/08856559.1938.10532520M. E. Herriott Scientific textbook selection, Science Education 17, no.22 (Apr 1933): 98–105.https://doi.org/10.1002/sce.3730170204 Chapter IV: Time Allotment and Grade Placement, Review of Educational Research 1, no.11 (Jun 2016): 30–36.https://doi.org/10.3102/00346543001001030

Multi-class AdaBoost

Boosting has been a very successful technique for solving the two-class classification problem. In going from two-class to multi-class classification, most algorithms have been restricted to reducing the multi-class classification problem to multiple two-class problems. In this paper, we propose a new algorithm that naturally extends the original AdaBoost algorithm to the multiclass case without reducing it to multiple two-class problems. Similar to AdaBoost in the twoclass case, this new algorithm combines weak classifiers and only requires the performance of each weak classifier be better than random guessing (rather than 1/2). We further provide a statistical justification for the new algorithm using a novel multi-class exponential loss function and forward stage-wise additive modeling. As shown in the paper, the new algorithm is extremely easy to implement and is highly competitive with the best currently available multi-class classification methods.