Improving ID3 Algorithm by Ignoring Minor Instances

Kaewrod, Nicha; Jearanaitanakij, Kietikul

doi:10.1109/icsec.2018.8712762

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…There are many works concerning the ID3 decision tree algorithm and their improvements like Es-Sabery et al [33]; Yu-Xun et al [41]; Chai et al [42]; Elyassami et al [43]; Zou et al [44]; Srinivasan et al [45]; Chen et al [46]; Ding et al [47]; Zhu et al [48]; Kaewrod et al [49]; Rajeshkanna et al [50]; and as introduced in the Table 3 and 4. In [33], we proposed a novel enhanced ID3 decision tree algorithm, which integrates the weighted theory and information gain criterion.…”

Section: Previous Researchmentioning

confidence: 99%

“…Zhu et al [48] It proposed a new improved ID3 which is based on information gain average with different parameters, to a specific range for avoiding the multi-valued attribute bias problem. The principal target of the work [49] is to evolve a new procedure to relieve the rigorousness of the traditional ID3 algorithm by removing minor examples. Rajeshkanna et al [50] implemented the ID3 technique using various UCI training datasets and it is also evaluated utilizing different statistical measures.…”

Section: Previous Researchmentioning

confidence: 99%

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A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

Es-sabery

Qadir

et al. 2021

IEEE Access

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Opinion Mining (OM) is a field of Natural Language Processing (NLP) that aims to capture human sentiment in the given text. With the ever-spreading of online purchasing websites, micro-blogging sites, and social media platforms, OM in online social media platforms has picked the interest of thousands of scientific researchers. Because the reviews, tweets and blogs acquired from these social media networks, act as a significant source for enhancing the decision making process. The obtained textual data (reviews, tweets, or blogs) are classified into three different class labels which are negative, neutral and positive for analyzing and extracting relevant information from the given dataset. In this contribution, we introduce an innovative MapReduce improved weighted ID3 decision tree classification approach for OM, which consists mainly of three aspects: Firstly We have used several feature extractors to efficiently detect and capture the relevant data from the given tweets, including N-grams or character-level, Bag-Of-Words, word embedding (GloVe, Word2Vec), FastText, and TF-IDF. Secondly, we have applied a multiple feature selector to reduce the high feature's dimensionality, including Chi-square, Gain Ratio, Information Gain, and Gini Index. Finally, we have employed the obtained features to carry out the classification task using an improved ID3 decision tree classifier, which aims to calculate the weighted information gain instead of information gain used in traditional ID3. In other words, to measure the weighted information gain for the current conditioned feature, we follow two steps: First, we compute the weighted correlation function of the current conditioned feature. Second, we multiply the obtained weighted correlation function by the information gain of this current conditioned feature. This work is implemented in a distributed environment using the Hadoop framework, with its programming framework MapReduce and its distributed file system HDFS. Its primary goal is to enhance the performance of a well-known ID3 classifier in terms of accuracy, execution time, and ability to handle the massive datasets. We have carried out several experiences that aims to assess the effectiveness of our suggested classifier compared to some other contributions chosen from the literature. The experimental results demonstrated that our ID3 classifier works better on COVID-19_Sentiments dataset than other classifiers in terms of Recall (85.72 %), specificity (86.51 %), error rate (11.18 %), false-positive rate (13.49 %), execution time (15.95s), kappa statistic (87.69 %), F1-score (85.54 %), classification rate (88.82 %), false-negative rate (14.28 %), precision rate (86.67 %), convergence (it convergent towards the iteration 90), stability (it is more stable with mean deviation standard equal to 0.12 %), and complexity (it requires much lower time and space computational complexity).

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Section: Previous Researchmentioning

confidence: 99%

Section: Previous Researchmentioning

confidence: 99%

A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

Es-sabery

Qadir

et al. 2021

IEEE Access

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“…Penggunaan tekhnologi informasi yang mampu mengelompokkan data wakaf dan membuat klasifikasikan secara rinci di harapakan mampu memecahkan masalah [4] yang ada .Penelitian ini menggunakan algoritma klasifikasi data mining [5] ID3 yang sangat populer dan memiliki akurasi yang tinggi . Kemampuan Algoritma ID3 untuk membuat pohon keputusan atau ID3 [6] secara sederhana dan mudah di pahami akan berguna menganalisa dan memprediksi kategori Wakaf yang sudah dikelola secara baik atau produktif dan yang belum produktif atau non produktif yang di kelola oleh Majelis Wakaf Yogyakarta. Dengan metode klasifikasi data mining algoritma ID3 [7] akan dilakukan prediksi dengan pengelompokan data, estimasi dengan memperhatikan kaidah dari asosiasi dari suatu data .…”

Section: Pendahuluanunclassified

Klasifikasi Wakaf Produktif Menggunakan Algoritma Id3 Pada Sistem Informasi Aset Dan Kehartabendaan Majelis Wakaf Yogyakarta

Munsarif

2020

JurTI

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– Productive Waqf is one of the waqf management programs developed in the modern era to improve the Islamic economy. The large number of waqf land received by nazir from year to year causes the management of waqf to be more complicated. This is caused by the lack of financial support and based on data from the ministry of religion in April 2020 the area of waqf land is 50,260.89 Ha. The waqf information system that has been developed has not been able to be a solution to the problems of waqf land management. Muhammadiyah which is the oldest Islamic organization that has succeeded in making many charitable efforts using waqf still not able to overcome the existing problems. The amount of waqf that is owned by Muhammadiyah requires an analysis to make a classification of productive and non-productive endowments that can provide information to make decisions in the management of endowment management on an ongoing basis. In this research, the ID3 algorithm is used for waqf classification which can show productive and non-productive categories using data derived from Muhamamdiyah Asset Information System in the Yogyakarta region. The results of the study provide recommendations that are shown in the decision tree produced by ID3 so that it can be concluded that for the management of waqf it is necessary to group and classify productive and non-productive land to create a professional endowment management plan.Keywords: Decision Support System, ID3 Algorithm, Data Mining, Waqf information system, Productive Waqf, Muhammadiyah, Rapidminner.Abstract – Wakaf produktif menjadi salah satu program pengelolaan wakaf yang dikembangkan di era modern dengan tujuan untuk meningkat perekonomian umat islam.Besarnya Jumlah wakaf tanah yang diterima oleh para nadzir dari tahun ke tahun menyebabkan pengelolaan wakaf menjadi lebih rumit.Hal ini di sebabkan tidak adanya dukungan pembiayaan dan pengembangannya.Berdasarkan data dari kementerian agama pada April 2020 luas tanah wakaf 50.260.89 Ha .Sistem informasi wakaf yang telah dikembangkan belum mampu menjadi solusi atas permasalah pengelolaan tanah wakaf .Muhammadiyah yang merupakan organisasi Islam tertua yang telah berhasil membuat banyak amal usaha dengan menggunakan wakaf masih belum mampu mengatasi persoalan yang ada.Besarnya wakaf yang di miliki oleh muhammadiyah membutuhkan sebuah analisa untuk membuat klasifikasi wakaf produktif dan non produktif yang mampu memberikan informasi untuk membuat keputusan dalam perencanaan pengelolaan wakaf secara berkesinambungan. Pada penelitian ini Algoritma ID3 digunakan untuk klasifikasi wakaf yang mampu menunjukan kategori produktif dan non produktif dengan menggunakan data yang berasal dari Sistem informasi aset Muhamamdiyah wilayah Yogyakarta. Hasil penelitian memberikan rekomendasi yang di tunjukkan dalam pohon keputusan yang di hasilkan oleh ID3 ,Sehingga bisa simpulkan bahwa untuk pengelolaan wakaf di butuhkan pengelompokan dan klasifikasi tanah produktif dan non produktif dalam upaya membuat rencana pengeloaan wakaf secara profesional.Kata kunci: Sistem Pendukung Keputusan, Algoritma ID3, Data Mining, Sistem Informasi Wakaf, Wakaf Porduktif, Muhammadiyah, Rapidminner

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Tweet Classification on the Base of Sentiments Using Deep Learning

SHIHAB>

Ekmekci

2023

Algorithms for Intelligent Systems

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Improving ID3 Algorithm by Ignoring Minor Instances

Cited by 4 publications

References 5 publications

A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

Klasifikasi Wakaf Produktif Menggunakan Algoritma Id3 Pada Sistem Informasi Aset Dan Kehartabendaan Majelis Wakaf Yogyakarta

Tweet Classification on the Base of Sentiments Using Deep Learning

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