Application of Naïve Bayes, Decision Tree, and K-Nearest Neighbors for Automated Text Classification

Breast cancer must be addressed by a multidisciplinary team aiming at the patient’s comprehensive treatment. Recent advances in science make it possible to evaluate tumor staging and point out the specific treatment. However, these advances must be combined with the availability of resources and the easy operability of the technique. This study is aimed at distinguishing and classifying benign and malignant cells, which are tumor types, from the data on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by applying data mining classification and clustering techniques with the help of the Weka tool. In addition, various algorithms and techniques used in data mining were measured with success percentages, and the most successful ones on the dataset were determined and compared with each other.

show abstract

“…Applied Bionics and Biomechanics posterior probabilities of PðC j | x i Þ and then PðC j | t i Þ are calculated by Bayes' theorem [12,13].…”

Section: Bayesian Classifiermentioning

confidence: 99%

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques

Mohammad

Teete

Al-Aaraj

et al. 2022

Applied Bionics and Biomechanics

View full text Add to dashboard Cite

show abstract

“…To further understand how to use them in the text classification, we assume that the task is to determine whether the given sentence is a negative or positive comment [54]. Like all machine learning models, the NB model also requires a training dataset containing a set of sorted sentences with their categories.…”

Section: Naive Bayes (Nb)mentioning

confidence: 99%

Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Hawraa Fadhil Khelil,

Mohammed Fadhil Ibrahim,

Hafsa Ataallah Hussein

et al. 2024

View full text Add to dashboard Cite

Customer opinion and reviews play a vital role in marketing expansion. Big companies all over the world assign a lot of their efforts to analyzing customers’ feedback to keep track of their needs. Natural Language Processing (NLP) is widely used to analyze such review texts. Arabic customer analysis and classification also began to gain researchers’ attention due to the wide range of Arabic language speakers. Working with Arabic Language is a very challenging task because of the orthographic nature of Arabic. Also, customers often write their reviews in their dialectical style, which often diverts from standard Arabic. This study presents a method to classify Arabic customer reviews using four classifiers (K-nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (RL), and Naïve Bayes (NB)). The classification is implemented with three stemming techniques (Snowball, Khoja, and Tashaphyne). The HARD dataset is adopted to perform the experiments. The results stated that the stemming methods can enhance classification performance despite the complexity of Arabic scripts and dialects. This work sheds light on utilizing and investigating more machine learning (ML) techniques and evaluating the results.

show abstract

“…In [25], the author compares the performances of three classification algorithms, namely KNN, NB and DT. He used the Saudi Press Agency (SPA) corpus.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning for Arabic Text Classification: A Comparative Study

Bouchiha¹,

Bouziane²,

Doumi

2022

Malaysian J. Sci. Adv. Tech.

View full text Add to dashboard Cite

The ultimate aim of Machine Learning (ML) is to make machine acts like a human. In particular, ML algorithms are widely used to classify texts. Text classification is the process of classifying texts into a predefined set of categories based on the texts’ content. It contributes to improving information retrieval on the Web. In this paper, we focus on the "Arabic" text classification since there is a large community in the world that uses this language. The Arabic text classification process consists of three main steps: preprocessing, feature extraction and ML algorithm. This paper presents a comparative empirical study to see which combination (feature extraction - ML algorithm) acts well when dealing with Arabic documents. So, we implemented one hundred sixty classifiers by combining 5 feature extraction techniques and 32 machine learning algorithms. Then, we made these classifiers open access for the benefit of the AI and NLP communities. Experiments were carried out using a huge open dataset. The comparison study reveals that TFIDF-Perceptron is the best performing combination of a classifier.

show abstract

Application of Naïve Bayes, Decision Tree, and K-Nearest Neighbors for Automated Text Classification

Cited by 17 publications

References 20 publications

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques

Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Machine Learning for Arabic Text Classification: A Comparative Study

Contact Info

Product

Resources

About