Optimization Scheme for Text Classification Using Machine Learning Naïve Bayes Classifier

Venkatesh,; Ranjitha, K. V.; Prasad, B. S. V.

doi:10.1007/978-981-15-1420-3_61

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…This algorithm has proven to be the most reliable classifier, particularly for smaller datasets. Ranjitha and Prasad in [14] presented different machine learning techniques like Hadoop map-reduce and naive Bayes classifiers to classify the data. Their demonstration revealed that Gaussian naive Bayes enhances text classification rates when compared to other machine learning approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

“…[13] 2020 Logistic Regression Improved classification rate on BBC news dataset. [14] 2020 Hadoop map-reduce and Naive Bayes classifiers Improved results on classification rate using Gaussian Naive Bayes.…”

Section: Hybrid Query Expansionmentioning

confidence: 99%

“…Our approach achieved a classification rate of 93%, with an error rate of only 7% when compared to existing literature, such as [12] which employs Decision Tree to enhance classification rates, and Shah et al in [13] who present a text classification model based on the Logistic Regression algorithm to improve classification results. We also compared our approach with the one presented in [14], which relies on Naive Bayes classifiers, and their studies show that Gaussian Naive Bayes improves classification rates. It's worth noting that none of these previously mentioned approaches from the literature used fuzzy logic in their text classification.…”

Section: Fuzzificationmentioning

confidence: 99%

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Optimization of the results of a multilingual search engine using a fuzzy recommendation approach

El Hadi,

Madani,

El Ayachi

et al. 2023

JIOS

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Search engines are now the main source for information retrieval due to the hugeexpansion of data on the internet over the last ten years. Providing users with the mostrelevant results for their queries poses a significant challenge for search engines.Semantic search engines, which go beyond traditional keyword-based searches, haveappeared as advanced information retrieval systems to address this problem. Thesesearch engines produce more precise and pertinent search results because theyunderstand the meanings of words and their relationships. They play a pivotal role inmanaging the vast amount of internet data, with a primary aim of enhancing searchprecision and user satisfaction. However, improving search precision remains as animportant goal for natural language processing researchers. The main objective of ourresearch is to improve the search engine results. We present a novel approach formeasuring the similarity between a user’s query and a list of documents within a searchengine. This approach provides a new fuzzy recommendation system using a syntacticand semantic similarity. Our results indicate that our method outperforms severalexisting approaches from the literature, achieving a high level of accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Hybrid Query Expansionmentioning

confidence: 99%

Section: Fuzzificationmentioning

confidence: 99%

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Optimization of the results of a multilingual search engine using a fuzzy recommendation approach

El Hadi,

Madani,

El Ayachi

et al. 2023

JIOS

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“…Hadoop Map Reduce is a parallel processing framework for big data that has been widely used as an OLAP (Online Analytic Processing) platform [7], [8]. Hadoop Map Reduce has also been widely used for text processing on big data [9].…”

Section: Introductionmentioning

confidence: 99%

Text Classification Using Genetic Programming with Implementation of Map Reduce and Scraping

Wedashwara

Irmawati

Wijayanto

et al. 2023

JOIV : Int. J. Inform. Visualization

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Classification of text documents on online media is a big data problem and requires automation. Text classification accuracy can decrease if there are many ambiguous terms between classes. Hadoop Map Reduce is a parallel processing framework for big data that has been widely used for text processing on big data. The study presented text classification using genetic programming by pre-processing text using Hadoop map-reduce and collecting data using web scraping. Genetic programming is used to perform association rule mining (ARM) before text classification to analyze big data patterns. The data used are articles from science-direct with the three keywords. This study aims to perform text classification with ARM-based data pattern analysis and data collection system through web-scraping, pre-processing using map-reduce, and text classification using genetic programming. Through web scraping, data has been collected by reducing duplicates as much as 17718. Map-reduce has tokenized and stopped-word removal with 36639 terms with 5189 unique terms and 31450 common terms. Evaluation of ARM with different amounts of multi-tree data can produce more and longer rules and better support. The multi-tree also produces more specific rules and better ARM performance than a single tree. Text classification evaluation shows that a single tree produces better accuracy (0.7042) than a decision tree (0.6892), and the lowest is a multi-tree(0.6754). The evaluation also shows that the ARM results are not in line with the classification results, where a multi-tree shows the best result (0.3904) from the decision tree (0.3588), and the lowest is a single tree (0.356).

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Content-Based Venue Recommender Approach for Publication

Umair

Jabbar

Rauf

et al. 2022

Communications in Computer and Information Science

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Optimization Scheme for Text Classification Using Machine Learning Naïve Bayes Classifier

Cited by 7 publications

References 8 publications

Optimization of the results of a multilingual search engine using a fuzzy recommendation approach

Optimization of the results of a multilingual search engine using a fuzzy recommendation approach

Text Classification Using Genetic Programming with Implementation of Map Reduce and Scraping

Content-Based Venue Recommender Approach for Publication

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