Optimizing forensic file classification: enhancing SFCS with βk hyperparameter tuning
D. Paul Joseph, Viswanathan Perumal
TL;DR
This paper introduces a new forensic file classification system that improves accuracy and efficiency by optimizing topic modeling parameters.
Contribution
The novel βk hyperparameter enhances seed word selection through semantic and contextual similarity evaluation.
Findings
The proposed SFCS system removed 278k irrelevant files and identified 5.6k suspicious files.
The model achieved 94.6% accuracy, 94.4% precision, and 96.8% recall.
The system operates within O(n log n) time complexity.
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…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Topic Modeling · Digital and Cyber Forensics