Enhanced text classification through an improved discrete laying chicken algorithm

Abstract

The exponential growth of digital text documents presents a significant challenge for text classification algorithms, as the vast number of words in each document can hinder their efficiency. Feature selection (FS) is a crucial technique that aims to eliminate irrelevant features and enhance classification accuracy. In this study, we propose an improved version of the discrete laying chicken algorithm (IDLCA) that utilizes noun‐based filtering to reduce the number of features and improve text classification performance. Although LCA is a newly proposed algorithm, it has not been systematically applied to discrete problems before. Our enhanced version of LCA employs different operators to improve both exploration and exploitation of this algorithm to find better solutions in discrete mode. To evaluate the effectiveness of the proposed method, we compared it with some conventional nature‐inspired feature selection methods using various learning models such as decision trees (DT), K‐nearest neighbor (KNN), Naive Bayes (NB), and support vector machine (SVM) on five benchmark datasets with three different evaluation metrics. The experimental results demonstrate the effectiveness of the proposed algorithm in comparison to the existing one. The code is available at https://github.com/m0javad/Improved-Discrete-Laying-Chicken-Algorithm.

Hub-and-Spoke Network Optimization with Flow Delay Cost: The Case of Goods Delivery on Urban Logistics Networks in Eastern China

“…Najy et al (2020) considered a novel and more realistic variant of the uncapacitated hub location problem where both flow-dependent economies of scale and congestion considerations are incorporated into the multiple-allocation version of the problem [17]. Daneshfar (2024) et al proposed an improved version of the discrete laying chicken algorithm (IDLCA) that utilizes noun-based filtering to reduce the number of features and improve text classification performance [18].…”

“…According to O'Kelly (1987) and Campbell and O'Kelly (2012), the H-S network can take on quite many configurations, some of which are shown in Figure 1 [5,18]. The simplest H-S network in Figure 1a is also called a star network, in which one hub connects trips and transfers flows for all O-D pairs.…”

Imbalanced Data Classification Based on Improved Random-SMOTE and Feature Standard Deviation

“…Specifically, due to the higher number of samples in the majority class, the model is more likely to learn the features of the majority class during the training process, resulting in the classifier being biased towards the majority class during prediction [6]. Bias toward majority class samples makes the classifier less capable of identifying minority class samples, and may even completely ignore the importance of the minority class, which can lead to misclassification in practical applications [7,8]. Therefore, an in-depth study of imbalanced classification algorithms is crucial to improve the performance and generalization ability of classifiers, especially when it comes to minority class samples with practical applications.…”