Analysis of Feature Weighting Methods Based on Feature Ranking Methods for Classification

Jankowski, Norbert; Usowicz, Krzysztof

doi:10.1007/978-3-642-24958-7_28

Cited by 14 publications

(9 citation statements)

References 14 publications

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“…The importance of attribute weighting in improving classification performance has been analysed by numerous works (Jankowski and Usowicz 2011;Kohavi et al 1997). Several authors (Leopold and Kindermann 2002;Lan et al 2005) have also stated that the selection of an adequate weighting function is even more important than the parameterisation of the kernel in SVM algorithms.…”

Section: Tag Weightingmentioning

confidence: 99%

Semantic grounding of social annotations for enhancing resource classification in folksonomies

Tommasel

Godoy

2014

J Intell Inf Syst

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User-generated annotations in tagging or bookmarking sites such as Flickr or Delicious can provide a promising and interesting source of information for aiding tasks such as Web resource classification. However, the use of tags brings up some challenges. Since there are no constraints on the terms that can be used for tagging, noise and ambiguity are introduced when users annotate resources. Moreover, traditional bag-of-words representations ignore connections between terms and, thus, are affected by synonymity and hyponymia. Althougth tag-based representations are a valuable source for classifying resources, the problems associated with the unsupervised nature of tags may hinder classification results. This paper presents an approach for semantically analysing social annotations in order to attain enriched concept-based representations of Web resources. Representations are enriched with concepts extracted from WordNet and Wikipedia to overcome problems caused by natural language as well as enhancing the quality of information available for performing an effective classification of resources. Several strategies for tag pre-processing, concept disambiguation and incorporation of semantic entities to representations are discussed and evaluated in this paper. Experimental results showed that the strategies proposed to associate tags with conceptual entities allow improving resource classification results, outperforming traditional approaches based on bag-of-words representations.

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Section: Tag Weightingmentioning

confidence: 99%

Semantic grounding of social annotations for enhancing resource classification in folksonomies

Tommasel

Godoy

2014

J Intell Inf Syst

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“…Several feature weight methods such as term frequency methods [12], feature rank methods [13], and self-adjustment methods [14,15] have been proposed and utilized in practice. Choosing appropriate feature weights can improve classification.…”

Section: Feature Weightsmentioning

confidence: 99%

“…Forty-four out of fortyfive experiments demonstrated that the proposed weighting process achieved a good performance on a dataset that included many irrelevant features. In [13], a new fast feature weighting algorithm was proposed based on feature ranking methods. The ranking methods involved correlations, information theory, and distance between probability distributions.…”

Section: Feature Weightsmentioning

confidence: 99%

“…For example, term frequency-inverse document frequency (TF-IDF) term weights are considered the attribute values of documents in many information retrieval models [11]. Overall, feature weighting methods can generally be divided into three types: term frequency methods [12], feature rank methods [13], and self-adjustment methods [14,15]. Term frequency represents the capacity of features for expressing document content, and it is computed by the 2 Security and Communication Networks number of times a feature is mentioned in a set of documents.…”

Section: Introductionmentioning

confidence: 99%

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Computing Adaptive Feature Weights with PSO to Improve Android Malware Detection

Zheng

et al. 2017

Security and Communication Networks

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Android malware detection is a complex and crucial issue. In this paper, we propose a malware detection model using a support vector machine (SVM) method based on feature weights that are computed by information gain (IG) and particle swarm optimization (PSO) algorithms. The IG weights are evaluated based on the relevance between features and class labels, and the PSO weights are adaptively calculated to result in the best fitness (the performance of the SVM classification model). Moreover, to overcome the defects of basic PSO, we propose a new adaptive inertia weight method called fitness-based and chaotic adaptive inertia weight-PSO (FCAIW-PSO) that improves on basic PSO and is based on the fitness and a chaotic term. The goal is to assign suitable weights to the features to ensure the best Android malware detection performance. The results of experiments indicate that the IG weights and PSO weights both improve the performance of SVM and that the performance of the PSO weights is better than that of the IG weights.

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“…Feature weighting is broadly used in batch learning [25,3] to assign different weights to feature accordingly to their relevance to the concept to be learned and improve prediction accuracy. As shown earlier, in opposition to static scenarios, the relevance of features may augment or diminish during a data stream, thus, techniques for detecting these changes are needed.…”

Section: Dynamic Feature Weightingmentioning

confidence: 99%

On Dynamic Feature Weighting for Feature Drifting Data Streams

Barddal

Gomes

Enembreck

et al. 2016

Machine Learning and Knowledge Discovery in Databases

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Analysis of Feature Weighting Methods Based on Feature Ranking Methods for Classification

Cited by 14 publications

References 14 publications

Semantic grounding of social annotations for enhancing resource classification in folksonomies

Semantic grounding of social annotations for enhancing resource classification in folksonomies

Computing Adaptive Feature Weights with PSO to Improve Android Malware Detection

On Dynamic Feature Weighting for Feature Drifting Data Streams

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