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https://github.com/alagoz/higec

HiGeC: Hierarchy Generation and Classification Framework

https://github.com/alagoz/higec

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Keywords

data-driven-class-hierarchy-generation gc hierarchical-classification hierarchical-clustering lcl lcn lcpn multiclass-classification xgboost-classifier
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HiGeC: Hierarchy Generation and Classification Framework

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  • Host: GitHub
  • Owner: alagoz
  • License: mit
  • Language: Python
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data-driven-class-hierarchy-generation gc hierarchical-classification hierarchical-clustering lcl lcn lcpn multiclass-classification xgboost-classifier
Created about 1 year ago · Last pushed 6 months ago
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README.md

fig_flowchart_small

HiGEC
Hierarchy Generation and Extended Classification Framework

Python
License
OpenML

HiGEC is a Python framework for enhancing multi-class classification through automated hierarchy generation (HG) and flexible hierarchy exploitation (HE) strategies. It supports hybrid approaches that integrate hierarchical and flat classifier outputs.

🔧 Installation ```bash git clone https://github.com/alagoz/higec.git cd higec pip install -r requirements.txt ``` **Dependencies:** `numpy` `scipy` `matplotlib` `scikit-learn` `scikit-learn-extra` `proglearn` `xgboost` `lightgbm` ---
⚡ Key Features � **Automatic hierarchy generation** from flat class labels 🧩 **Hybrid HE+F classification strategies** 🖇️ Support for **any scikit-learn compatible classifier** 📊 **Benchmark-ready** with OpenML integration 🌳 **Visualization tools** for hierarchy inspection ---
🚀 Quick Start Run the example: ```bash python run_higec_example.py ``` Pipeline: 1. Downloads OpenML dataset 2. Trains flat classifier baseline 3. Generates class hierarchy 4. Evaluates hierarchical approach ---
🛠 Core Components | File | Purpose | |------------|-----------------------------------| | `HG.py` | Hierarchy generation | | `HE.py` | Hierarchy exploitation | | `hdc.py` | Divisive clustering | | `utils.py` | Data handling & visualization | ---
🧪 Customization Adjust parameters in 'run_higec_example.py': ```bash DID = 46264 # OpenML dataset ID HiGEC = 'CCM[HAC|COMPLETE]-LCPN[ETC]+F[XGB]' # HG + HE scheme CLF_NAME_FC = 'RF' # Flat classifier ``` Available classifiers: `RF`, `XGB`, `ETC`, `LGB`. ---
📈 Example Output ```bash Extended Linkage Table: node_id:0, node_type:parent, subsets:[[0], [1,2,3,4]], branch_ids:[0,7], parent_id:None node_id:1, node_type:parent, subsets:[[3,4],[1,2]], branch_ids:[5,6], parent_id:0 ``` ```bash Performance Comparison: - Flat Classification (RF) (f1): 0.3517 in 0.4309 seconds - HiGEC: CCM[HAC|COMPLETE]-LCPN[ETC]+F[XGB] (f1): 0.3700 in 1.1853 seconds ``` Generated Hierarchy: ![example_hierarchy](https://github.com/user-attachments/assets/96e78795-541b-41a1-a7bb-a945b65411fa) ---
📊 Benchmark Results HiGEC was evaluated on **100 multi-class tabular datasets**, showing consistent F1-score gains over flat classification (FC), particularly with hybrid HE+F configurations. --- ### Mean F1 Comparison (HiGEC vs FC) fig_mcm_higec_vs_fc ### Mean F1 Scores & Standard Deviations ![table](https://github.com/user-attachments/assets/b7b5a00c-e597-4576-bc19-b1d881d66541) --- **Download raw results (F1 scores per dataset):** - [f1_scores_fc_vs_higec.csv](./results/f1_scores_fc_vs_higec.csv) – Contains per-dataset F1-scores of FC and selected 9 HiGEC algorithms. - Columns: `index`, `short`, `RF`, `XGB`, `ETC`, `LGB`, `LCN[XGB]+`, `LCPN[ETC]+F[XGB]`, `LCPN[RF]+F[XGB]`, `LCPN[XGB]+F[RF]`, `LCL[XGB]+F[RF]`, `LCPN[RF]+F[RF]`, `LCL[RF]+F[XGB]`, `LCPN[LGB]+F[XGB]`, `LCPN[XGB]+F[XGB]` **Download mean performance metrics for all FC algorithms:** - [fc_mean_performance.csv](./results/fc_mean_performance.csv) – Contains mean scores across datasets for each FC algorithm. - Columns: `index`, `short`, `mean_f1_xgb`, `mean_f1_catb`, ... , `mean_acc_xgb`, `mean_acc_catb`, ... , `mean_auc_xgb`, `mean_auc_catb`, ... , `total_dur_xgb`, `total_dur_catb`, ... These CSV files allow full reproducibility and further statistical analysis of HiGEC’s performance compared to FC. ---
📖 References For more details on methodology, datasets, and evaluations, see the HiGEC GitHub repository.

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  • Name: Celal ALAGÖZ
  • Login: alagoz
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