stdClass Object
(
    [id] => 17735
    [paper_index] => 202510-01-024287
    [title] => UNSUPERVISED MACHINE LEARNING APPROACHES FOR ANOMALY DETECTION IN HIGH-DIMENSIONAL DATA
    [description] => 
    [author] => Sanika Thete
    [googlescholar] => 
    [doi] => 
    [year] => 2025
    [month] => October
    [volume] => 11
    [issue] => 10
    [file] => fm/jpanel/upload/2025/October/202510-01-024287.pdf
    [abstract] => Detecting anomalies in high-dimensional, highly imbalanced transaction data is critical for financial security. This study evaluates three unsupervised approaches — Isolation Forest, One-Class SVM, and a deep Autoencoder — on the Kaggle Credit Card Fraud Detection dataset (284,807 transactions; 492 fraudulent; ≈0.172% fraud). Raw features (Time, Amount) were standardized and a 70:30 train–test split was used; unsupervised models were trained without label information and assessed post-hoc using precision, recall, F1-score, and ROC-AUC. The Autoencoder achieved the best discrimination (ROC-AUC ≈ 0.96) and high recall for rare fraud cases; Isolation Forest provided a strong balance of performance and interpretability (ROC-AUC ≈ 0.94); One-Class SVM performed acceptably (ROC-AUC ≈ 0.91) but scaled poorly. Supervised baselines (Logistic Regression and Random Forest with SMOTE) reached ROC-AUC ≈ 0.97 and ≈ 0.956, respectively, but rely on labeled data and showed unfavorable precision–recall trade-offs. We discuss deployment considerations (computational cost, interpretability, and real-time processing) and recommend a hybrid pipeline: use Isolation Forest or Autoencoder for initial screening and a supervised verifier for high-confidence alerts. The proposed framework enhances detection of rare fraudulent events while controlling false positives, making it practical for operational fraud-detection systems.
    [keywords] => Anomaly detection; Unsupervised learning; Autoencoder; Isolation Forest; One-Class SVM; Credit card fraud
    [doj] => 2025-10-07
    [hit] => 
    [status] => 
    [award_status] => P
    [orderr] => 7
    [journal_id] => 1
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Multidisciplinary Research (IJMR)
    [short_code] => IJMR
    [eissn] =>  2455-3662 (Online)
    [pissn] => - --
    [home_page_wrapper] => images/products_image/11.IJMR.png
)
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