ArXiv - Predicting Scientific Paper Category

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MSc Data & Web Science, Aristotle University of Thessaloniki (AUTH)

Course: Advanced Machine Learning

Project: “Predicting Categories of Scientific Papers with Advanced Machine Learning Techniques”

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Team Members:

1. Georgios Arampatzis
2. Alexia Fytili
3. Eleni Tsiolaki

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Dataset: ArXiv

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Technical Report: PDF

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Imbalance:

Oversampling: SMOTE, Borderline SMOTE, RandomOverSampler

Undersampling: Tomek Links, Random Undersampling, NearMiss1, NearMiss2, NearMiss3

Over&Undersampling: SMOTE and Tomek Links

Extra: Generating Synonym Text

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Multi-label Classification:

• LabelPowerset
• BinaryRelevance
• ClassifierChain
• MLkNN

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Interpretability / Explainability:

• ELI5
• LIME
• Anchors
• SHAP

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Code Structure:

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└── arxiv_predicting_paper_category
    ├── Imbalanced
    │   ├── imbalance_generate_synonym_text.py
    │   ├── imbalance_methods.py
    │   └── random_undesampling.py
    ├── Interpretability
    │   ├── interpretability_results
    │   │   ├── Weights_CountVectorizer_25000_LinearSVC_Big_dataset.html
    │   │   ├── Weights_CountVectorizer_25000_LinearSVC_TOMEK_Big_dataset.html
    │   │   ├── Weights_CountVectorizer_25000_LogisticRegression_Big_dataset.html
    │   │   ├── Weights_CountVectorizer_25000_LogisticRegression_TOMEK_Big_dataset.html
    │   │   └── shap_LinearSVC.png
    │   ├── LIME.py
    │   ├── anchors_explanations.py
    │   ├── eli5.py
    │   └── shap_explanations.py
    ├── Multi_label_classification
    │   ├── multi_label_classification_new.py
    │   └── multi_label_plots.py
    ├── data_pics
    │   ├── imbalanced_cv.png
    │   ├── imbalanced_dataset.png
    │   └── imbalanced_roc.png
    ├── dataset
    │   ├── create_synonym_dataset.py
    │   ├── dataset_methods.py
    │   └── exploratory_data_analysis.py
    ├── preprocessing
    │   ├── create_csv_with_categories_as_new_Columns.py
    │   ├── create_multi_class_dataset.py
    │   ├── create_preprocessed_csv.py
    │   ├── create_tf_idf_csv.py
    │   ├── dictionaries.py
    │   ├── filter_dataset_based_on_category.py
    │   └── main_preprocessing.py
    ├── .gitingnore
    └── README.md