DuBE is an ensemble learning framework for (multi)class-imbalanced classification. It is an easy-to-use solution to imbalanced learning problems, features good performance, computing efficiency, and wide compatibility with different learning models. Documentation and examples are available at https://duplebalance.readthedocs.io.

• Table of Contents
• Background
• Install
• Usage
• Documentation

Imbalanced Learning (IL) is an important problem that widely exists in data mining applications. Typical IL methods utilize intuitive class-wise resampling or reweighting to directly balance the training set. However, some recent research efforts in specific domains show that class-imbalanced learning can be achieved without class-wise manipulation. This prompts us to think about the relationship between the two different IL strategies and the nature of the class imbalance. Fundamentally, they correspond to two essential imbalances that exist in IL: the difference in quantity between examples from different classes as well as between easy and hard examples within a single class, i.e., inter-class and intra-class imbalance.

Existing works fail to explicitly take both imbalances into account and thus suffer from suboptimal performance. In light of this, we present Duple-Balanced Ensemble, namely DUBE, a versatile ensemble learning framework. Unlike prevailing methods, DUBE directly performs inter-class and intra-class balancing without relying on heavy distance-based computation, which allows it to achieve competitive performance while being computationally efficient.

Our DuBE implementation requires following dependencies:

• python (>=3.8.5)
• numpy (>=1.19.2)
• pandas (>=1.1.3)
• scikit-learn (>=0.23.2)

You can install DuBE by clone this repository:

git clone https://github.com/AnonAuthorAI/duplebalance.git
cd duplebalance
pip install .

For more detailed usage example, please see Examples.

A minimal working example:

# load dataset & prepare environment
from duplebalance import DupleBalanceClassifier
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=1000, n_classes=3,
                           n_informative=4, weights=[0.2, 0.3, 0.5],
                           random_state=0)

# ensemble training
clf = DupleBalanceClassifier(
    n_estimators=10,
    random_state=42,
    ).fit(X_train, y_train)

# predict
y_pred_test = clf.predict_proba(X_test)

For more detailed API references, please see API reference.

Our DupleBalance implementation can be used much in the same way as the ensemble classifiers in sklearn.ensemble. The DupleBalanceClassifier class inherits from the sklearn.ensemble.BaseEnsemble base class.

Main parameters are listed below: