A Approach for Text Classification Feature Dimensionality Reduction and Rule Generation on Rough Set

Yin, Shiqun; Huang, Zhixing; Lu, Chen; Qiu, Yuhui

doi:10.1109/icicic.2008.7

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…First, storing so many features requires a vast amount of memory storage. Second, high dimensionality might lead to worse performance or even overfitting, when dimensionality increases while training samples remain fixed [ 37 , 38 ]. Overfitting might be a direct result of this so called curse of dimensionality.…”

Section: Methodsmentioning

confidence: 99%

Finding Dutch natives in online forums

Boom

Veenman

2018

Forensic Sciences Research

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Law enforcement agencies have a restricted area in which their powers apply, which is called their jurisdiction. These restrictions also apply to the Internet. However, on the Internet, the physical borders of the jurisdiction, typically country borders, are hard to discover. In our case, it is hard to establish whether someone involved in criminal online behavior is indeed a Dutch citizen. We propose a way to overcome the arduous task of manually investigating whether a user on an Internet forum is Dutch or not. More precisely, we aim to detect that a given English text is written by a Dutch native author. To develop a detector, we follow a machine learning approach. Therefore, we need to prepare a specific training corpus. To obtain a corpus that is representative for online forums, we collected a large amount of English forum posts from Dutch and non-Dutch authors on Reddit. To learn a detection model, we used a bag-of-words representation to capture potential misspellings, grammatical errors or unusual turns of phrases that are characteristic of the mother tongue of the authors. For this learning task, we compare the linear support vector machine and regularized logistic regression using the appropriate performance metrics f 1 score, precision, and average precision. Our results show logistic regression with frequency-based feature selection performs best at predicting Dutch natives. Further study should be directed to the general applicability of the results that is to find out if the developed models are applicable to other forums with comparable high performance.

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Section: Methodsmentioning

confidence: 99%

Finding Dutch natives in online forums

Boom

Veenman

2018

Forensic Sciences Research

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“…For S=(U,A,V,f), if the research object attribute set A consists of a conditional attribute C and a decision attribute D , that is, A=C80%true∪D,C80%true∩D=Φ, then the information system S is called the decision table [12].…”

Section: Rough Set Theorymentioning

confidence: 99%

Fire Control System Operation Status Assessment Based on Information Fusion: Case Study

Wang

2019

Sensors

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In traditional fault diagnosis strategies, massive and disordered data cannot be utilized effectively. Furthermore, just a single parameter is used for fault diagnosis of a weapons fire control system, which might lead to uncertainty in the results. This paper proposes an information fusion method in which rough set theory (RST) is combined with an improved Dempster–Shafer (DS) evidence theory to identify various system operation states. First, the feature information of different faults is extracted from the original data, then this information is used as the evidence of the state for a diagnosis object. By introducing RST, the extracted fault information is reduced in terms of the number of attributes, and the basic probability value of the reduced fault information is obtained. Based on an analysis of conflicts in the existing DS evidence theory, an improved conflict evidence synthesis method is proposed, which combines the improved synthesis rule and the conflict evidence weight allocation methods. Then, an intelligent evaluation model for the fire control system operation state is established, which is based on the improved evidence theory and RST. The case of a power supply module in a fire control computer is analyzed. In this case, the state grade of the power supply module is evaluated by the proposed method, and the conclusion verifies the effectiveness of the proposed method in evaluating the operation state of a fire control system.

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“…It is advantageous in improving the efficiency of the selected feature subset and suitable for high-volume text classification. The accuracy is higher and the speed of classification is faster than the classification based on vector space comparison [6].…”

Section: Related Workmentioning

confidence: 99%

Quality Assessment in Vocational Education Based on a Hierarchical Classification Technique

Ekwong

Wichain

Haruechaiyasak

2011

AMR

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According to the laws of education in Thailand, the Office for National Education Standards and Quality Assessment is responsible for assessing the external educational institutes in order to develop the quality and educational standards. The external quality assessment reports are represented in both structured and unstructured data. In this paper, we focus on the analysis of unstructured data, i.e., to automatically classify strength and weakness points. We propose and evaluate two different classification models: Flat Classification and Hierarchical Classification. Three algorithms, Naive Bayes, Support Vector Machines (SVM) and Decision Tree, were used in the experiments. The results showed that classification viathe Hierarchical Classification model by using the SVM yielded the best performance. The classification of strength and weakness points yielded the F-measure equal to 0.843 and 0.893, respectively. The proposed approach can be applied as a decision support function for quality assessment in vocational education.

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A Approach for Text Classification Feature Dimensionality Reduction and Rule Generation on Rough Set

Cited by 6 publications

References 16 publications

Finding Dutch natives in online forums

Finding Dutch natives in online forums

Fire Control System Operation Status Assessment Based on Information Fusion: Case Study

Quality Assessment in Vocational Education Based on a Hierarchical Classification Technique

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