Towards Language Independent Automated Learning of Text Categorization Models

Apté, Chidanand; Damerau, Fred J.; Weiss, Sholom M.

doi:10.1007/978-1-4471-2099-5_3

Cited by 87 publications

(64 citation statements)

References 4 publications

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“…Most methods for feature subset selection that are used information retrieval and text-learning (eg. [1], [3], [11]) are very simple compared to the methods developed in machine learning. Basically, some scoring measure that is used on a single feature is selected, a score is assigned to each feature independently, features are sorted according to the assigned score and a predefined number of the best features is taken to form the solution feature subset.…”

Section: F E a T U R E S U B S E T S E L E C T I O N Approachesmentioning

confidence: 99%

Feature subset selection in text-learning

Mladenić

1998

Machine Learning: ECML-98

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Abstract. This paper describes several known and some new methods for feature subset selection on large text data. Experimental comparison given on real-world data collected from Web users shows that characteristics of the problem domain and machine learning algorithm should be considered when feature scoring measure is selected. Our problem domain consists of hyperlinks given in a form of small-documents represented with word vectors. In our learning experiments naive Bayesian classifier was used on text data. The best performance was achieved by the feature selection methods based on the feature scoring measure called Odds ratio that is known from information retrieval. I n t r o d u c t i o nIn propositional learning problem domain is given by a set of examples, where each example is described with a class value and a vector of feature values. Features used to describe examples are not necessary all relevant and beneficial for the inductive learning and may reduce quality of induced model. Additionally, a high number of features may slow down the induction process while giving similar results as obtained with much smaller feature subset. Section 2 describes approach commonly used for feature subset selection in learning on text data (text-learning). In Section 4 we experimentally compare different feature scoring measures on real-world data collected from Web users. Section 3 describes our problem domain and naive Bayesian classifier for text that we used in experiments. Discussion is given in Section 5. 2F e a t u r e s u b s e t s e l e c t i o n approaches Different methods have been developed and used for feature subset selection in statistics, p~ttern recognition and machine learning, using different search strategies and evaluation functions. John et al. [4] pointed out the difference between the two main approaches used in machine learning to feature subset selection: filtering approach where the feature subset is selected independent of the learning method and wrapper approach where the feature subset is selected using the same learning algorithm that will be used for learning on domain represented with the selected feature subset.

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Section: F E a T U R E S U B S E T S E L E C T I O N Approachesmentioning

confidence: 99%

Feature subset selection in text-learning

Mladenić

1998

Machine Learning: ECML-98

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“…Naïve Bayes, assume the features as independent components. Hence, rule learning based classifiers are context sensitive classifiers (Apte et al 1994;Cohen and Singer 1996).…”

Section: Rule Learning Based Classifiersmentioning

confidence: 99%

Web Page Classification*

Choi¹,

Yao²

2005

Studies in Fuzziness and Soft Computing

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“…In this experiment we used the partition of Reuters-21450 that was prepared by Apté, Damerau, and Weiss (1994) for their experiments with the SWAP-1 rule learner. The Reuters-21450 corpus was partitioned into a training set of 7,789 documents and a test set containing 3,309 document.…”

Section: An Experiments With a Large Number Of Classesmentioning

confidence: 99%

“…(See, for instance, Apté, Damerau, & Weiss (1994), Biebricher et al (1988), Cohen & Singer (1996), Field (1975), Fuhr & Pfeifer (1994), Koller & Sahami (1997), Lewis & Ringuette (1994), Moulinier, Raškinis, & Ganascia (1996), Ng, Goh, & Low (1997) and Yang (1994)). It would be impossible for us to compare our algorithms to all of the previous methods.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2000

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Abstract. This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text categorization tasks. We present results comparing the performance of BoosTexter and a number of other text-categorization algorithms on a variety of tasks. We conclude by describing the application of our system to automatic call-type identification from unconstrained spoken customer responses.

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Towards Language Independent Automated Learning of Text Categorization Models

Cited by 87 publications

References 4 publications

Feature subset selection in text-learning

Feature subset selection in text-learning

Web Page Classification*

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