Term-relevance computations and perfect retrieval performance

Shaw, Jr. W. M.

doi:10.1016/0306-4573(95)00011-5

Cited by 43 publications

(18 citation statements)

References 9 publications

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“…One of the most popular models used in information retrieval is the vector model [1,8,9]. Various effective retrieval techniques have been developed for this model and among them is the method of relevance feedback.…”

Section: Related Workmentioning

confidence: 99%

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A unified framework for semantics and feature based relevance feedback in image retrieval systems

Lü

Hu²,

Zhu

et al. 2000

Proceedings of the Eighth ACM International Conference on Multimedia

185

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The relevance feedback approach to image retrieval is a powerful technique and has been an active research direction for the past few years. Various ad hoc parameter estimation techniques have been proposed for relevance feedback. In addition, methods that perform optimization on multi-level image content model have been formulated. However, these methods only perform relevance feedback on the low-level image features and fail to address the images' semantic content. In this paper, we propose a relevance feedback technique, iFind, to take advantage of the semantic contents of the images in addition to the low-level features. By forming a semantic network on top of the keyword association on the images, we are able to accurately deduce and utilize the images' semantic contents for retrieval purposes. The accuracy and effectiveness of our method is demonstrated with experimental results on real-world image collections.

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Section: Related Workmentioning

confidence: 99%

“…This technique is implemented in the MARS system [6]. Experiments show that the retrieval performance can be improved considerably by using relevance feedback [1,8,9].…”

Section: Related Workmentioning

confidence: 99%

A unified framework for semantics and feature based relevance feedback in image retrieval systems

Lü

Hu²,

Zhu

et al. 2000

Proceedings of the Eighth ACM International Conference on Multimedia

185

View full text Add to dashboard Cite

show abstract

“…We handle singularities as proposed in [13]. Notice that Odds ratio is not averaged over all feature values, like Information gain, but only the value recording that word occurred is considered.…”

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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“…To handle the singularities, that is, when any of the A, B, C, or D is zero, following the approach proposed by Shaw JR [24], the OR is replaced with:…”

Section: Odds Ratiomentioning

confidence: 99%

An evaluation study on text categorization using automatically generated labeled dataset

Zhu

Wong

2017

Neurocomputing

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Naïve Bayes, k-Nearest Neighbours, Adaboost, Support Vector Machines and Neural Networks are five among others commonly used text classifiers. Evaluation of these classifiers involves a variety of factors to be considered including benchmark used, feature selections, parameter settings of algorithms, and the measurement criteria employed. Researchers have demonstrated that some algorithms outperform others on some corpus, however, inconsistency of human labelling and high dimensionality of feature spaces are two issues to be addressed in text categorization. This paper focuses on evaluating the five commonly used text classifiers by using an automatically generated text document collection which is labelled by a group of experts to alleviate subjectivity of human category assignments, and at the same time to examine the influence of the number of features on the performance of the algorithms.

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Term-relevance computations and perfect retrieval performance

Cited by 43 publications

References 9 publications

A unified framework for semantics and feature based relevance feedback in image retrieval systems

A unified framework for semantics and feature based relevance feedback in image retrieval systems

Feature subset selection in text-learning

An evaluation study on text categorization using automatically generated labeled dataset

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