Document-Base Extraction for Single-Label Text Classification

Wang, Yanbo J.; Sanderson, Robert; Coenen, Frans; Leng, Paul

doi:10.1007/978-3-540-85836-2_34

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…The preparation of Usenet Articles ("20 Newsgroups") based documentbases adopted the approach of Deng et al [8], where the entire collection was randomly split into two document-bases covering 10 classes each: 20NG.D10000.C10 and 20NG.D9997.C10. The preparation of Reuters-21578 and the MedLine-OHSUMED document-bases recalled the idea of Wang et al [25], where the Reuters.D6643.C8 and OHSUMED.D6855.C10 document-bases were generated.…”

Section: Resultsmentioning

confidence: 99%

A Hybrid Statistical Data Pre-processing Approach for Language-Independent Text Classification

Wang¹,

Coenen

Sanderson

2009

Advanced Data Mining and Applications

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Abstract. Data pre-processing is an important topic in Text Classification (TC).It aims to convert the original textual data in a data-mining-ready structure, where the most significant text-features that serve to differentiate between textcategories are identified. Broadly speaking, textual data pre-processing techniques can be divided into three groups: (i) linguistic, (ii) statistical, and (iii) hybrid (i) & (ii). With regard to language-independent TC, our study relates to the statistical aspect only. The nature of textual data pre-processing includes: Document-base Representation (DR) and Feature Selection (FS). In this paper, we propose a hybrid statistical FS approach that integrates two existing (statistical FS) techniques, DIAAF (Darmstadt Indexing Approach Association Factor) and GSSC (Galavotti⋅Sebastiani⋅Simi Coefficient). Our proposed approach is presented under a statistical "bag of phrases" DR setting. The experimental results, based on the well-established associative text classification approach, demonstrate that our proposed technique outperforms existing mechanisms with respect to the accuracy of classification.

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Section: Resultsmentioning

confidence: 99%

A Hybrid Statistical Data Pre-processing Approach for Language-Independent Text Classification

Wang¹,

Coenen

Sanderson

2009

Advanced Data Mining and Applications

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“…Based on the probability of a particular answer a i ∈ S a , yielding a certain utility u given the indicators x 0 and x 1 , the joint probability h of a tuple x 0 , x 1 , u is determined. If the statistical properties of the components computing x 0 and x 1 are unknown, classification accuracies found in the literature (Wang et al, 2008) or heuristics are helpful means to estimate h. In such environments the incorporation of user feedback and machine learning techniques helps refining h and therefore yields more accurate query strategies. If the joint probability function and the cost estimates are accurate, STS will return an optimal query strategy (MacQueen, 1964).…”

Section: Common Utility Mass Functionmentioning

confidence: 99%

Optimizing queries to remote resources

Weichselbraun

2010

J Intell Inf Syst

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One key property of the Semantic Web is its support for interoperability. Recent research in this area focuses on the integration of multiple data sources to facilitate tasks such as ontology learning, user query expansion and context recognition. The growing popularity of such machups and the rising number of Web APIs supporting links between heterogeneous data providers asks for intelligent methods to spare remote resources and minimize delays imposed by queries to external data sources.This paper suggests a cost and utility model for optimizing such queries by leveraging optimal stopping theory from business economics: applications are modeled as decision makers that look for optimal answer sets. Queries to remote resources cause additional cost but retrieve valuable information which improves the estimation of the answer set's utility. Optimal stopping optimizes the trade-off between query cost and answer utility yielding optimal query strategies for remote resources. These strategies are compared to conventional approaches in an extensive evaluation based on real world response times taken from seven popular Web services.

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“…This approach used supervised learning where a feature may be connected with multiple labels. It is opposed to single-label classification when each feature is associated with a single class (label) [7]. Moreover, MLC is extensively applied in real-world problems, such as bioinformatics, e-commerce…etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification of Learning Objects Using Clustering Algorithms Based on Feature Selection

Amane¹,

Aissaoui²,

Berrada

2022

Int. J. Emerg. Technol. Learn.

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In the field of online learning, the development of learning objects (LOs) has increased. LOs promote reusing and referencing educational content in various learning environments. However, despite this progress, the lack of a conceptual model for sharing suitable LOs between learners makes multiple challenges. In this regard, multi-label classification plays a significant role to make high-quality LOs, which can be accessible and reusable. This article highlights a new way of using learning objects based on Multi-Label Classification (MLC) and clustering algorithms with feature selection techniques. It suggests a new system that makes the most suitable choice among many alternative sources based on the Sharable Content Object Reference Model (SCORM). The proposed algorithm has been tested on a real-world application dataset related to the data analysis service for the learning science community. The experimental results show that our algorithm outperforms the traditional approach and produces good results.

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Document-Base Extraction for Single-Label Text Classification

Cited by 5 publications

References 15 publications

A Hybrid Statistical Data Pre-processing Approach for Language-Independent Text Classification

A Hybrid Statistical Data Pre-processing Approach for Language-Independent Text Classification

Optimizing queries to remote resources

Multi-Label Classification of Learning Objects Using Clustering Algorithms Based on Feature Selection

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