A Comparative Study on Term Weighting Schemes for Text Classification

Mazyad, Ahmad; Teytaud, Fabien; Fonlupt, Cyril

doi:10.1007/978-3-319-72926-8_9

Cited by 8 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study done by Lan et al in [6] confirmed the superiority of tf.idf over tf.chi. A recent and fair comparaison beween state of the art TWS [7] have shown similar results as shown in [3]. However, In [4], Deng et al concluded unlike Debole the superiority tf.chi over tf.idf .…”

Section: Introductionmentioning

confidence: 66%

Information Gain Based Term Weighting Method for Multi-label Text Classification Task

Mazyad

Teytaud

Fonlupt

2018

Advances in Intelligent Systems and Computing

Self Cite

View full text Add to dashboard Cite

In text classification, terms are given weights using Term Weighting Scheme (TWS) in order to improve classification performance. Multi-label classification task are generaly simplified into several single-label binary task. Thus, the term distribution are considered only in terms of positive and negative categories. In this paper, we propose a new TWS based on the information gain measure for multi-label classification task. This TWS try to overcome this shortness without affecting the complexity of the problem. In this paper, we examine our proposed TWS with eight well-known TWS on two popular problems using 5 learning algorithms. From our experimental results, our new proposed method outperforms other methods specialy regarding the macro-averaging measure.

show abstract

Section: Introductionmentioning

confidence: 66%

Information Gain Based Term Weighting Method for Multi-label Text Classification Task

Mazyad

Teytaud

Fonlupt

2018

Advances in Intelligent Systems and Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since the unsupervised term weighting methods are used extensively in exam question classification, therefore only the result by using unsupervised term weighting methods will be analyzed. In [19], the authors conduct their comparative study by using different unsupervised term weighting methods. The unsupervised term weighting methods used in this study are TF and TF-IDF.…”

Section: Related Work In Text Classificationmentioning

confidence: 99%

Identifying Optimal Baseline Variant of Unsupervised Term Weighting in Question Classification Based on Bloom Taxonomy

Sangodiah

San

Fui

et al. 2022

mendel

View full text Add to dashboard Cite

Examination is one of the common ways to evaluate the students’ cognitive levels in higher education institutions. Exam questions are labeled manually by educators in accordance with Bloom’s taxonomy cognitive domain. To ease the burden of the educators, several past research works have proposed the automated question classification based on Bloom’s taxonomy using the machine learning technique. Feature selection, feature extraction and term weighting are common ways to improve the accuracy of question classification. Commonly used term weighting method in the past work is unsupervised namely TF and TF-IDF. There are several variants of TF and TFIDF and the most optimal variant has yet to be identified in the context of question classification based on BT. Therefore, this paper aims to study the TF, TF-IDF and normalized TF-IDF variants and identify the optimal variant that can enhance the exam question classification accuracy. To investigate the variants two different classifiers were used, which are Support Vector Machine (SVM) and Naïve Bayes. The average accuracies achieved by TF-IDF and normalized TF-IDF variants using SVM classifier were 64.3% and 72.4% respectively, while using Naïve Bayes classifier the average accuracies for TF-IDF and normalized TF-IDF were 61.9% and 63.0% respectively. Generally, the normalized TF-IDF variants outperformed TF and TF-IDF variants in accuracy and F1-measure respectively. Further statistical analysis using t-test and Wilcoxon Signed also shows that the differences in accuracy between normalized TF-IDF and TF, TF-IDF are significant. The findings from this study show that the Normalized TF-IDF3 variant recorded the highest accuracy of 74.0% among normalized TF-IDF variants. Also, the differences in accuracy between Normalized TF-IDF3 and other normalized variants are generally significant, thus the optimal variant is Normalized TF-IDF3. Therefore, the normalized TF-IDF3 variant is important for benchmarking purposes, which can be used to compare with other term weighting techniques in future work.

show abstract

“…Unsupervised TWSs are generally borrowed from Information Retrieval domain [26] and adopted for TC [22,7,23].…”

Section: Term Weighting Schemesmentioning

confidence: 99%

“…Besides the raw count (f t,d ) representation of tf , there exist numerous other variants such as binary representation (w i,j = 1 if the term t i occurs in the document d j and 0 otherwise), log(f i,j ) + 1, f i,j / t ∈d f t ,d . All these variants are also used as TWS on their own [26,22,7,8]. The inverse document frequency has also a number of variants such as log(N/ [26].…”

Section: Term Weighting Schemesmentioning

confidence: 99%

See 1 more Smart Citation

Generating Term Weighting Schemes Through Genetic Programming

Mazyad

Teytaud

Fonlupt

2019

Machine Learning, Optimization, and Data Science

Self Cite

View full text Add to dashboard Cite

Term-Weighting Scheme (TWS) is an important step in text classification. It determines how documents are represented in Vector Space Model (VSM). Even though state-of-the-art TWSs exhibit good behaviors, a large number of new works propose new approaches and new TWSs that improve performances. Furthermore, it is still difficult to tell which TWS is well suited for a specific problem. In this paper, we are interested in automatically generating new TWSs with the help of evolutionary algorithms and especially genetic programming (GP). GP evolves and combines different statistical information and generates a new TWS based on the performance of the learning method. We experience the generated TWSs on three well-known benchmarks. Our study shows that even early generated formulas are quite competitive with the state-of-the-art TWSs and even in some cases outperform them.

show abstract

A Comparative Study on Term Weighting Schemes for Text Classification

Cited by 8 publications

References 14 publications

Information Gain Based Term Weighting Method for Multi-label Text Classification Task

Information Gain Based Term Weighting Method for Multi-label Text Classification Task

Identifying Optimal Baseline Variant of Unsupervised Term Weighting in Question Classification Based on Bloom Taxonomy

Generating Term Weighting Schemes Through Genetic Programming

Contact Info

Product

Resources

About