Optimized TF-IDF Algorithm with the Adaptive Weight of Position of Word

Chen, Jie; Liang, Yi

doi:10.2991/aiie-16.2016.28

Cited by 19 publications

(14 citation statements)

References 8 publications

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“…After all high frequency patterns have been found, then the association rules that meet the minimum requirements for confidence are searched by calculating the associative rule confidence A  B, as for the calculation using Eq. (11). (11) Furthermore, this stage will be useful to find all the rules of association in each class that meets the threshold (for example MinConfidence = 0.7).…”

Section: Proposed Methods Implementationmentioning

confidence: 99%

“…(11). (11) Furthermore, this stage will be useful to find all the rules of association in each class that meets the threshold (for example MinConfidence = 0.7). For implementation is shown in (Table-VIII).…”

Section: Proposed Methods Implementationmentioning

confidence: 99%

“…Then modified TF-IDF schemes are introduced which consider the count of terms lost from the document as a factor in calculating the existing term weights namely mTF, mTFIDF, TFmIDF, and mTFmIDF [1]. Jie Chen, et al [11]proposes an adaptive weighting of keyword positions following the traditional TF-IDF algorithm, called the TF-IDF-AL Algorithm. This algorithm can utilize the internal characteristics of key documents.…”

Section: Imroatul Khuluqi Izzah Abba Suganda Girsangmentioning

confidence: 99%

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Association on Supervised Term Weighting Method for Classification on Data Twitter

Izzah,

Girsang

2020

IJRTE

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Term weighting is a preprocessing phase that has an important role in the text classification by giving the appropriate weight for each term in all documents. In previous research, many supervised term weighting methods have been introduced, but most of the supervised term weighting only considers the distribution of terms in the two classes so that it is not optimal for the multi-class classification. This paper introduces a new supervised weighting with association concept to optimize term weighting distributions in multi-class cases by considering terms that exist in each class and paying attention to the number of terms in the document belonging to the class, also considering the relationship pattern between one or more items with association concept in a dataset to measure the strength of terms in a class by using confidence values. The dataset used are the data twitter taken from the PR FM twitter account. The proposed supervised term weighting method implemented with SVM classifier can outperform unsupervised weighting schemes such as TF-IDF with the average accuracy 81.704%.

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Section: Proposed Methods Implementationmentioning

confidence: 99%

Section: Proposed Methods Implementationmentioning

confidence: 99%

Section: Imroatul Khuluqi Izzah Abba Suganda Girsangmentioning

confidence: 99%

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Association on Supervised Term Weighting Method for Classification on Data Twitter

Izzah,

Girsang

2020

IJRTE

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“…The output of this method really depends on the input from the user. Content-based filtering is one of the best method to build a recommendation system with text as their datasets [9].…”

Section: Content-based Filteringmentioning

confidence: 99%

Recommendation System for Thesis Topics Using Content-based Filtering

Kusuma¹,

Musdholifah²

2021

Indonesian J. Comput. Cybern. Syst.

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When pursuing their bachelor degree, every students are required to pursue a thesis in order to graduate from the major that they take. However, during the process, students got several difficulty regarding chosing their thesis topics. Therefore, a recommendation system is needed to classify thesis topics based on the students’ interest and abilities. This study developed a recommendation system for thesis topics using content-based filtering where the students will be asked to choose the course that they interested in along with their grades. After getting all the required data, the recommendation system will process the data and then it’ll show the title and the abstract of publication that fits the criteria.In this research, there are 2 datasets that is used, there are lecturer publication within 3 years and syllabus data of Computer Science UGM course. After running this research, it was found that the recommendation system has an average 7.46 seconds running time. It was also found that the recommendation system got an average 83% of the recommendation system objectives. The recommendation system objectives consist of relevance, novelty, serendipity, and increasing recommendation diversity.

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“…where n loc is the location of the i−th feature in the order. Moreover, the more forward the location is in the order, the more important the feature is in the description [25]. We use β as the nal weight to determine which task line the request should be scheduled.…”

Section: Classi Cation Modelmentioning

confidence: 99%

SRAF: A Service-Aware Resource Allocation Framework for VM Management in Mobile Data Networks

Liu

Zheng

Zhang

et al. 2018

Mobile Information Systems

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Service latency and resource utilization are the key factors which limit the development of mobile data networks. To this end, we present a service-aware resource allocation framework, called SRAF, to allocate the basic resources by managing virtual machine (VM). In SRAF, we design two new methods for better virtual machine (VM) management. Firstly, we propose the self-learning classification algorithm (SCA) which executes the service request classification. Then, we use the classification results to schedule different types of VMs. Secondly, we design a sharing mode to jointly execute service requests, which can share the CPU and bandwidth simultaneously. In order to enhance the utilization of resources with the sharing mode, we also design two scaling algorithms, i.e., the horizontal scaling and the vertical scaling, which execute the operation of resource-level scaling and VM-level scaling, respectively. Furthermore, to enhance the stability of SRAF and avoid the frequent operation of scaling, we introduce a Markov decision process (MDP) to control VM migration. The experimental results reveal that SRAF greatly reduces service latency and enhances resource utilization. In addition, SRAF also has a good performance on stability and robustness for different situations of congestion.

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Optimized TF-IDF Algorithm with the Adaptive Weight of Position of Word

Cited by 19 publications

References 8 publications

Association on Supervised Term Weighting Method for Classification on Data Twitter

Association on Supervised Term Weighting Method for Classification on Data Twitter

Recommendation System for Thesis Topics Using Content-based Filtering

SRAF: A Service-Aware Resource Allocation Framework for VM Management in Mobile Data Networks

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