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Optimizing forensic file classification: enhancing SFCS with βk hyperparameter tuning.

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Title: Optimizing forensic file classification: enhancing SFCS with βk hyperparameter tuning.
Authors: Joseph, D. Paul, Perumal, Viswanathan
Source: PeerJ Computer Science; Mar2025, p1-27, 27p
Subject Terms: FORENSIC sciences, CLASSIFICATION, CONCEPTUAL models, INFORMATION storage & retrieval systems, TEXT mining, INFORMATION retrieval, MACHINE learning
Abstract: In forensic topical modelling, the α parameter controls the distribution of topics in documents. However, low, high, or incorrect values of α lead to topic sparsity, model overfitting, and suboptimal topic distribution. To control the word distribution across topics, the β parameter is introduced. However, low, high, or inappropriate β values lead to sparse distribution, disjointed topics, and abundant highly probable words. The βj parameter, in conjunction with seed-guided words based on Term Frequency and Inverse Document Frequency, is introduced to address the issues. Nevertheless, the data often suffers from skewness or noise due to frequent co-occurrences of unrelated polysemic word pairs generated using Pointwise Mutual Information. By integrating α, β, and βj into file classification systems, classification models converge to local optima with O(n log n* |V|) time complexity. To combat these challenges, this research proposes the SDOT Forensic Classification System (SFCS) with a functional parameter βk that identifies seed words by evaluating semantic and contextual similarity of word vectors. As a result, the topic distribution (Θd) is compelled to model the curated seed words within the distribution, generating pertinent topics. Incorporating βk into SFCS allowed the proposed model to remove 278 k irrelevant files from the corpus and identify 5.6 k suspicious files by extracting 700 blacklisted keywords. Furthermore, this research implemented hyperparameter optimization and hyperplane maximization, resulting in a file classification accuracy of 94.6%, 94.4% precision and 96.8% recall within O(n log n) complexity. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:In forensic topical modelling, the α parameter controls the distribution of topics in documents. However, low, high, or incorrect values of α lead to topic sparsity, model overfitting, and suboptimal topic distribution. To control the word distribution across topics, the β parameter is introduced. However, low, high, or inappropriate β values lead to sparse distribution, disjointed topics, and abundant highly probable words. The β<subscript>j</subscript> parameter, in conjunction with seed-guided words based on Term Frequency and Inverse Document Frequency, is introduced to address the issues. Nevertheless, the data often suffers from skewness or noise due to frequent co-occurrences of unrelated polysemic word pairs generated using Pointwise Mutual Information. By integrating α, β, and β<subscript>j</subscript> into file classification systems, classification models converge to local optima with O(n log n* |V|) time complexity. To combat these challenges, this research proposes the SDOT Forensic Classification System (SFCS) with a functional parameter β<subscript>k</subscript> that identifies seed words by evaluating semantic and contextual similarity of word vectors. As a result, the topic distribution (Θ<subscript>d</subscript>) is compelled to model the curated seed words within the distribution, generating pertinent topics. Incorporating β<subscript>k</subscript> into SFCS allowed the proposed model to remove 278 k irrelevant files from the corpus and identify 5.6 k suspicious files by extracting 700 blacklisted keywords. Furthermore, this research implemented hyperparameter optimization and hyperplane maximization, resulting in a file classification accuracy of 94.6%, 94.4% precision and 96.8% recall within O(n log n) complexity. [ABSTRACT FROM AUTHOR]
ISSN:23765992
DOI:10.7717/peerj-cs.2608