Maximizing text-mining performance

Weiss, Sholom M.; Apté, Chidanand; Damerau, Fred J.; Johnson, David; Oles, Frank J.; Goetz, Thomas; Hampp, Thomas

doi:10.1109/5254.784086

Cited by 157 publications

(63 citation statements)

References 9 publications

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“…They obtained the results comparable to the best results of Support Vector Machines and k-Nearest Neighbor methods [26,25,20], and better performing than Sleeping-experts, Rocchio, Naive Bayes and PrIF/DF [22].…”

Section: Introductionsupporting

confidence: 64%

Boosting to correct inductive bias in text classification

Liu

Yang

Carbonell

2002

Proceedings of the Eleventh International Conference on Information and Knowledge Management

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This paper studies the effects of boosting in the context of different classification methods for text categorization, including Decision Trees, Naive Bayes, Support Vector Machines (SVMs) and a Rocchio-style classifier. We identify the inductive biases of each classifier and explore how boosting, as an error-driven resampling mechanism, reacts to those biases. Our experiments on the Reuters-21578 benchmark show that boosting is not effective in improving the performance of the base classifiers on common categories. However, the effect of boosting for rare categories varies across classifiers: for SVMs and Decision Trees, we achieved a 13-17% performance improvement in macro-averaged F1 measure, but did not obtain substantial improvement for the other two classifiers. This interesting finding of boosting on rare categories has not been reported before.

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Section: Introductionsupporting

confidence: 64%

Boosting to correct inductive bias in text classification

Liu

Yang

Carbonell

2002

Proceedings of the Eleventh International Conference on Information and Knowledge Management

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“…O parâmetro k pode ser obtido através de validação cruzada na porção de treino, maximizando-se indicadores de performance de classificação como sensibilidade e especificidade, entre outros. Maiores detalhes sobre KNN podem ser obtidos em Duda et al [17], enquanto que exemplos de aplicações são encontrados em Golub et al [18], Weiss et al [19] e Chaovalitwongse et al [20].…”

Section: Referencial Teóricounclassified

Seleção De Variáveis Para Categorização De Amostras Químicas

Anzanello

2017

Eclet. Quim. J.

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This paper presents a method to select the best variables to categorize chemical samples into two classes, say conforming or non-conforming. For that matter, PLS regression is combined with a data mining tool, the k-Nearest Neighbor classification technique, through an iterative variable selection process. The recommended subset of variables is chosen based on several criteria: sensitivity, specificity and percent of retained variables. When applied to two datasets related to wine analysis and one associated to QSAR, the proposed method significantly reduced the number of variables required for classification, while yielding superior categorization performance when compared to using all original variables.

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“…Every classifier has its own inductive bias which affects the predictive performance. Research results indicate that ensembles perform better than single classifier in fields of text categorization [11] and data classification, etc. Several rules such as majority voting, i.e., bagging [4], weighted combination where weights represent effectiveness of member classifiers such as boosting [4], dynamic classifier selection [12], [13] and veto voting [9], [10] can be used for combining the decisions and having a final prediction.…”

Section: Veto-based Classificationmentioning

confidence: 99%