Enhancement of the A.B. Hassanat ensemble approach k nearest neighbor 9EA-KNN) algorithm applied in twitter profanity filter. 6

By: Carl Vince C. Aggabao, Aries L. Dela Cruz, Mike Miguel O. Gomez. 4 0 16, [, ] | [, ] |
Contributor(s): 5 6 [] |
Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; 4538346Edition: Description: Content type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:
Contents:
Action note: In: Summary: ABSTRACT; This study aims to develop and evaluate an enhanced Ensemble Average K-Nearest Neighbor (EA-KNN) algorithm for text classification tasks, focusing on its performance in handling varying levels of data noise and computational complexity. The research employed a systematic approach to algorithm development and evaluation. Initially, the enhanced EA-KNN aqlgorithm was designed, integrating techniques such as XGBoost and KD-tree to improve classification accuracy and computational efficiency. Subsequently, the algorithm was evaluated using multiple datasets sourced from Twitter, representing different levels of noise in the data. The evaluation metrics results revealed promising performance of the enhanced EA-KNN algorithm, with high accuracy, recall, F1 score, and precision across different datasets. Additionally, the algorithm demonstrated resilience to data noise, maintaining robust performance even at increased noise levels. Furthermore, computational complexity per predictions was significantly reduced compared to the existing approach, indicating improved efficiency. The findings suggest that the enhanced EA-KNN algorithm holds great potential for practical applications in text classification tasks, particularly in scenarios with noisy data. Its ability to maintain high accuracy and efficiency makes it a valuable tool for content moderation and user safety in online platforms. Other editions:
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)

Undergraduate Thesis : (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2024. 56

5

ABSTRACT; This study aims to develop and evaluate an enhanced Ensemble Average K-Nearest Neighbor (EA-KNN) algorithm for text classification tasks, focusing on its performance in handling varying levels of data noise and computational complexity. The research employed a systematic approach to algorithm development and evaluation. Initially, the enhanced EA-KNN aqlgorithm was designed, integrating techniques such as XGBoost and KD-tree to improve classification accuracy and computational efficiency. Subsequently, the algorithm was evaluated using multiple datasets sourced from Twitter, representing different levels of noise in the data. The evaluation metrics results revealed promising performance of the enhanced EA-KNN algorithm, with high accuracy, recall, F1 score, and precision across different datasets. Additionally, the algorithm demonstrated resilience to data noise, maintaining robust performance even at increased noise levels. Furthermore, computational complexity per predictions was significantly reduced compared to the existing approach, indicating improved efficiency. The findings suggest that the enhanced EA-KNN algorithm holds great potential for practical applications in text classification tasks, particularly in scenarios with noisy data. Its ability to maintain high accuracy and efficiency makes it a valuable tool for content moderation and user safety in online platforms.

5

There are no comments for this item.

to post a comment.

© Copyright 2024 Phoenix Library Management System - Pinnacle Technologies, Inc. All Rights Reserved.