raj-1411 / adbet-woa

Adaptive Beta Hill Climbing Aided Whale Optimization Algorithm (AdBet-WOA)

Adaptive $\beta$-Hill Climbing Aided Whale Optimization Algorithm (AdBet-WOA)

• This repository contains the official implementation of our paper titled "Deep Feature Selection using Adaptive $\beta$-Hill Climbing aided Whale Optimization Algorithm for Lung and Colon Cancer Detection" published in Biomedical Signal Processing and Control, Elsevier.

Overview

• AdBet-WOA is a hybrid meta-heuristic optimization algorithm using the Adaptive $\beta$-Hill Climbing local search property as the exploitation agent in the popular Whale Optimization Algorithm.
• The overall flowchart of AdBet-WOA and its Pseudo-algorithm are given as follows :

  
  

• The end-to-end implementation of our proposed algorithm on the LC25000 dataset is shown as follows :

  
  

Dependencies

directory> pip install -r requirements.txt

Arguments

directory\AltWOA> python main.py -h
usage: main.py [-h] [--number_classes NUMBER_CLASSES] [--train_path TRAIN_PATH] [--validation_path VALIDATION_PATH] [--test_path TEST_PATH] [--epoch EPOCH]
               [--lrate LRATE] [--batch_size_training BATCH_SIZE_TRAINING] [--batch_size_validation BATCH_SIZE_VALIDATION] [--load_model_wts LOAD_MODEL_WTS] [-- population_size POPULATION_SIZE] [--number_mutations NUMBER_MUTATIONS] [--epoch_for_algo EPOCH_FOR_ALGO]

optional arguments:
  -h, --help            show this help message and exit
  --number_classes NUMBER_CLASSES   Class distribution of data
  --train_path TRAIN_PATH
                        Path to train data
  --validation_path VALIDATION_PATH
                        Path to validation data
  --test_path TEST_PATH
                        Path to test data
  --epoch EPOCH   Number of iterations
  --lrate LRATE   Learning rate
  --batch_size_training BATCH_SIZE_TRAINING
                        Send batch size for training
  --batch_size_validation BATCH_SIZE_VALIDATION
                        Send batch size for validation and testing
  --load_model_wts LOAD_MODEL_WTS
                        True: if model weights are required to be loaded
  --population_size POPULATION_SIZE
                        Set population size
  --population_for_mating POPULATION_FOR_MATING
                        Set population for mating
  --number_mutations NUMBER_MUTATIONS
                        Number of mutations
  --epoch_for_algo EPOCH_FOR_ALGO
                        Set the epoch count for algorithm

Code Execution

directory/main.py -num_class {} -train {} -validation {} -test {} -epoch {} -lr {} -batch_tr {} -batch_val {} -load {} -pop_size {} -pop_mating {} -num_mut {} -epoch_algo {}

Description

directory
     |
     +--> utils
     |      |
     |      +--> data.py              # The python file which performs the dataloader initialization for train, val and test
     |      +--> feature_space.py     # The python file used to store the generated features in csv files
     |      +--> trainer.py           # The python file for training the dataset with our complete framework
     |
     +--> algorithm
     |      |
     |      +--> adbetwoa.py          # The python file which contains our entire proposed algorithm
     |      +--> algo_utils.py        # The python file containing other necessary peripheral functions related to algorithm
     |      +--> csv_read.py          # The python file for reading the csv files storing generated features
     |      +--> transf_functions.py  # The python file with other peripheral methods
     |
     +--> dos_class
     |      |
     |      +--> resnet_encoder.py    # The python file which generated the features for two-class classification
     |
     +--> tres_class
     |      |
     |      +--> effnet_encoder.py    # The python file which generated the features for three-class classification
     |
     +--> cinco_class
     |      |
     |      +--> effnet_encoder.py    # The python file which generated the features for five-class classification
     |
     +--> model_weights               # Folder to save the wiights of the best performing model 
     |
     |
     |                                  
     +--> main.py                    # The main function which reads the arguments dependiong on user input and performs the whole 
                                       end-to-end operation in a chronological order