SDD SMOTE with incremental boosting

This SMOTE variant, known as SDD SMOTE with incremental boosting, uses the same data generation logic as in SDD SMOTE, but in addition to it, it uses incremental boosting as an ensemble learning approach to improve data quality and accuracy.

from sdgne.datagenerator.smote import SDD_BoostCC

minority_column_label = 'class'
minority_class_label = 0

synthesizer = SDD_BoostCC(dataset,minority_column_label,minority_class_label)

synthesize_data = synthesizer.data_generator()

Importing SMOTE

from sdgne.datagenerator.smote import SDD_BoostCC

---

Creating a synthesizer

synthesizer = SDD_BoostCC(dataset, minority_column_label, minority_class_label)

Parameters

dataset	required	pd.Dataframe	Represents a pandas data frame containing both, the original minority and the original majority data.
minority_column_label	required	string	Represents the column label. Eg. 'class', 'output'
minority_class_label	required	string	Represents the minority class label. Eg. '1', '0'

Returns

An instance of class SDD_BoostCC.

Synthetic Data Generation

data_generator()

The data generator function generates synthetic data using the synthesizer. It has an option parameter num_to_synthesize.

• If num_to_synthesize is not defined, data_generator by default generates the `n` number of synthetic data such that the majority data and minority datasets get balanced.

• If num_to_synthesize is defined and the dataset is already balanced, data_generator generates 2 * Number of original minority data.

• If num_to_synthesize is defined and the dataset is not balanced, it generates synthetic data equal to the value passed.

synthesized_data = synthesizer.data_generator(no_of_syntetic_data)

Parameters

num_to_synthesize	optional (default: None)	integer	Represents the number of synthetic data to be generated.

Returns

A pandas data frame that combines original data and synthetic data.

Usage

synthesizer.data_generator()

synthesizer.data_generator(num_to_synthesize=100)