Prototype-Based Soft Feature Selection Package

Nana A. Otoo

This repository contains the code for the paper Prototype-Based Soft Feature Selection Package

Abstract

This paper presents a prototype-based soft feature selection package (Sofes) wrapped around the highly interpretable Matrix Robust Soft Learning Vector Quantization (MRSLVQ) and the Local MRSLVQ algorithms. The process of assessing feature relevance with Sofes aligns with a comparable approach established in the Nafes package, with the primary distinction being the utilization of prototype-based induction learners influenced by a probabilistic framework. The numerical evaluation of test results aligns Sofes’ performance with that of the Nafes package. https://vixra.org/abs/2308.0112

The implementation requires Python >=3.6 . The author recommends to use a virtual environment or Docker image. The details of the implementation and results evaluation can be found in the paper.

To install the Python requirements use the following command:

pip install -r requirements.txt 

To replicate results for WDBC in the paper run the default parameters:

python train.py --dataset wdbc --model mrslvq --eval_type ho
python train.py --dataset wdbc --model mrslvq --eval_type mv
python train.py --dataset wdbc --model lmrslvq --eval_type ho --reject_option
python train.py --dataset wdbc --model lmrslvq --eval_type mv --reject_option

To replicate results for Ozone Layer in the paper run the default parameter:

python train.py --dataset ozone --model mrslvq --eval_type ho
python train.py --dataset ozone --model mrslvq --eval_type mv
python train.py --dataset ozone --model lmrslvq --eval_type ho --reject_option
python train.py --dataset ozone --model lmrslvq --eval_type mv --reject_option

usage: train.py [-h] [--ppc PPC] [--dataset DATASET] [--model MODEL]
                [--sigma SIGMA] [--regularization REGULARIZATION]
                [--eval_type EVAL_TYPE] [--max_iter MAX_ITER]
                [--verbose VERBOSE] [--significance] [--norm_ord NORM_ORD]
                [--evaluation_metric EVALUATION_METRIC]
                [--perturbation_ratio PERTURBATION_RATIO]
                [--termination TERMINATION]
                [--perturbation_distribution PERTURBATION_DISTRIBUTION]
                [--reject_option] [--epsilon EPSILON]