Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm

Networks are strained by spam, which also overloads email servers and blocks mailboxes with unwanted messages and files. Setting the protective level for spam filtering might become even more crucial for email users when malicious steps are taken since they must deal with an increase in the number of valid communications being marked as spam. By finding patterns in email communications, spam detection systems (SDS) have been developed to keep track of spammers and filter email activity. SDS has also enhanced the tool for detecting spam by reducing the rate of false positives and increasing the accuracy of detection. The difficulty with spam classifiers is the abundance of features. The importance of feature selection (FS) comes from its role in directing the feature selection algorithm’s search for ways to improve the SDS’s classification performance and accuracy. As a means of enhancing the performance of the SDS, we use a wrapper technique in this study that is based on the multi-objective grasshopper optimization algorithm (MOGOA) for feature extraction and the recently revised EGOA algorithm for multilayer perceptron (MLP) training. The suggested system’s performance was verified using the SpamBase, SpamAssassin, and UK-2011 datasets. Our research showed that our novel approach outperformed a variety of established practices in the literature by as much as 97.5%, 98.3%, and 96.4% respectively.©2022 the Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

AUTO-ADAPTIVE THE WEIGHT IN BATCH BACK PROPAGATION ALGORITHM VIA DYNAMIC LEARNING RATE

Abstract Batch back propagation (BBP) algorithm is commonly used in many applications, including robotics, automation, and global positioning systems. The man drawbacks of batch back propagation (BBP) algorithm is slow training, and there are several parameters needs to be adjusted manually, also suffers from saturation training. The objective of this study is to improve the speed uptraining of the BBP algorithm and to remove the saturation training. To overcome these problems, we have created a new dynamic learning rate to escape the local minimum, which enables a faster training time. We presented dynamic batch backpropagation algorithm (DBBPLR) which training with dynamic learning rate. This technique was implemented using a sigmoid function. The XOR problem, the Balance dataset, and the Iris dataset were used as benchmarks with different structures to test the efficiency of the dynamic learning rate. The real datasets were divided into a training set and a testing set, and 75 experiments were carried out using Matlab software2016a. From the experimental results, it can be shown that the DBBPLR algorithm provides superior performance over the existing BBP algorithm in terms of training, the speed of training, time training, number of epochs and accuracy training and also with existing work