## A Re-Examination of Text Categorization Methods (1999)

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Citations: | 653 - 19 self |

### BibTeX

@MISC{Yang99are-examination,

author = {Yiming Yang and Xin Liu},

title = {A Re-Examination of Text Categorization Methods},

year = {1999}

}

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### Abstract

This paper reports a controlled study with statistical significance tests on five text categorization methods: the Support Vector Machines (SVM), a k-Nearest Neighbor (kNN) classifier, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a NaiveBayes (NB) classifier. We focus on the robustness of these methods in dealing with a skewed category distribution, and their performance as function of the training-set category frequency. Our results show that SVM, kNN and LLSF significantly outperform NNet and NB when the number of positive training instances per category are small (less than ten), and that all the methods perform comparably when the categories are sufficiently common (over 300 instances).

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Citation Context ... most commonly investigated application domains in the TC literature. An increasing number of learning approaches have been applied, including regression models[9, 32], nearest neighbor classification=-=[17, 29, 33, 31, 14]-=-, Bayesian probabilistic approaches [25, 16, 20, 13, 12, 18, 3], decision trees[9, 16, 20, 2, 12], inductive rule learning[1, 5, 6, 21], neural networks[28, 22], on-line learning[6, 15] and Support Ve... |

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Citation Context ...a wellknown statistical approach which has been intensively studied in pattern recognition for over four decades[8]. kNN has been applied to text categorization since the early stages of the research =-=[17, 29, 11]-=-. It is one of the the top-performing methods on the benchmark Reuters corpus (the 21450 version, Apte set); the other top-performing methods include LLSF by Yang, decision trees with boosting by Apte... |

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Citation Context ...ing approaches have been applied, including regression models[9, 32], nearest neighbor classification[17, 29, 33, 31, 14], Bayesian probabilistic approaches [25, 16, 20, 13, 12, 18, 3], decision trees=-=[9, 16, 20, 2, 12]-=-, inductive rule learning[1, 5, 6, 21], neural networks[28, 22], on-line learning[6, 15] and Support Vector Machines [12]. While the rich literature provides valuable information about individual meth... |

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Citation Context ...em's assignments (n \Theta m). 3 Classifiers 3.1 SVM Support Vector Machines (SVM) is a relatively new learning approach introduced by Vapnik in 1995 for solving two-class pattern recognition problems=-=[27]-=-. It is based on the Structural Risk Minimization principle for which error-bound analysis has been theoretically motivated[27, 7]. The method is defined over a vector space where the problem is to fi... |

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Citation Context ...1 for Tsd s:e:(sd) ; otherwise, the standard normal distribution is used instead. 4.4 Macro t-test after rank transformation To compare systems A and B based on the F1 values after rank transformation=-=[4]-=-, in which the F1 values of the two systems on individual categories are pooled together and sorted, then these values are replaced by the corresponding ranks. To make a distinction from the T-test ab... |

11 |
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Citation Context ...nother open question for TC research is how robust methods are in solving problems with a skewed category distribution. Since categories typically have an extremely nonuniform distribution in practice=-=[30]-=-, it would be meaningful to compare the performance of different classifiers with respect to category frequencies, and to measure how much the effectiveness of each method depends on the amount of dat... |

7 | Expert network: E ective and e cient learning from human decisions in text categorization and retrieval - Yang - 1994 |

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