Robust Text Classifier for Classification of Spam E-Mail Documents with Feature Selection Technique

Shrivas, Akhilesh Kumar; Dewangan, Amit Kumar; Ghosh, Samrendra Mohan

doi:10.18280/isi.260502

Cited by 3 publications

(2 citation statements)

References 26 publications

(29 reference statements)

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“…RF is one of the ML techniques, a classification of decision trees enhanced from the Classification and Regression Trees (CART) method [23]. RF is capable of handling large datasets and has large input features [24]. Picture of the RF classifier workflow as shown in Figure 3.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Comparison of Machine Learning Performance Using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns

Fadlil¹,

Riadi²,

Putra³

2023

RIA

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Batik is a work of art from Indonesia that has many types and pattern. One of the batik producing areas is Surakarta, the famous pattern in this area are Sawat, Sementrante, and Satriomanah. The problem that arises is the difficulty of distinguishing the three existing pattern because they have a high level of similarity. Therefore, this research aims to solve these problems using NB and RF methods. As a feature extraction, a Gray Level Cooccurrence Matrix is used as a texture feature extraction. The research phase includes methods for dataset collection, preprocessing, feature extraction, and classification. These two methods, RF and NB, can be used as methods for batik fabric classification. The most accurate result obtained by the RF method was 97.91% accurate in dataset A, while the NB method was 96.66% accurate on the same dataset. According to the research results, it is found that the RF method outperforms the NB method in classifying the types of batik patterns.

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Section: Random Forest (Rf)mentioning

confidence: 99%

Comparison of Machine Learning Performance Using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns

Fadlil¹,

Riadi²,

Putra³

2023

RIA

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“…The NB algorithm comprises three types: Gaussian, Multinomial, and Bernoulli. The choice of the NB classifier depends on the distribution of input features and the nature of the problem being addressed [8]. The Gaussian algorithm is suitable when the input features follow a normal distribution [9].…”

Section: Introductionmentioning

confidence: 99%

Using Natural Language Processing for Programming Language Code Classification with Multinomial Naive Bayes

Odeh,

Odeh

et al. 2023

RIA

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and others. Leveraging natural language processing concepts, this research explores the application of the Multinomial Naï ve Bayes (MNB) algorithm to identify and classify programming languages used in source code files. MNB is a relatively simple and fast algorithm for text classification. The study utilizes a dataset comprising 12 programming languages and consists of 12,003 samples, totaling 396,090 lines of code. The MNB algorithm is trained on this diverse dataset, and its performance in classifying programming language source code is evaluated. The results of the study demonstrate an impressive accuracy rate of 95.09% in accurately identifying and classifying programming languages. This high accuracy highlights the effectiveness of the applied NLP techniques, specifically the MNB algorithm, in the classification task. The findings of this research have significant implications for multiprogramming language editors such as Visual Studio Code and Notepad+ or any programming editor. With the automatic recognition of programming languages enabled by this approach, users can conveniently paste source code into these editors, and the system will automatically identify and classify the programming language being used. This functionality enhances the user experience and streamlines the coding process, particularly in multi-language development environments.

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Using Multinomial Naive Bayes Machine Learning Method To Classify, Detect, And Recognize Programming Language Source Code

Odeh

2022

2022 International Arab Conference on Information Technology (ACIT)

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Robust Text Classifier for Classification of Spam E-Mail Documents with Feature Selection Technique

Cited by 3 publications

References 26 publications

Comparison of Machine Learning Performance Using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns

Comparison of Machine Learning Performance Using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns

Using Natural Language Processing for Programming Language Code Classification with Multinomial Naive Bayes

Using Multinomial Naive Bayes Machine Learning Method To Classify, Detect, And Recognize Programming Language Source Code

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