Performance Augmentation of Cuckoo Search Optimization Technique Using Vector Quantization in Image Compression

Article Computer Science, Artificial Intelligence

Region Purity-Based Local Feature Selection: A Multiobjective Perspective

Yu Zhou et al.

Summary: In contrast to traditional feature selection methods, local feature selection methods partition the sample space and obtain feature subsets for each local region. However, most existing local feature selection algorithms lack a problem-specific objective function and instead use a distance-like objective function, leading to limited classification performance. In this article, we propose a novel objective function called region purity (RP) for local feature selection. To solve this problem, we use an improved nondominated sorting genetic algorithm III and develop a regional feature sharing strategy. Experimental results on various datasets demonstrate the effectiveness of our proposed RP-LFS. Compared to other state-of-the-art feature selection and local feature selection algorithms, RP-LFS achieves competitive classification accuracy while reducing the feature subset size.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2023)

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Article Computer Science, Information Systems

An efficient face anti-spoofing and detection model using image quality assessment parameters

Aditya Bakshi et al.

Summary: Biometric authentication faces a significant problem of intruders using reconstructed or fake samples to access legitimate traits. The demand for safety in mobile devices, such as smartphones and tablets, has also increased the importance of biometric security. This paper proposes a novel fake biometric detection technique using liveness detection to differentiate between real and fake traits in a face recognition system. The technique utilizes different image quality assessment parameters to achieve low-complexity detection.

MULTIMEDIA TOOLS AND APPLICATIONS (2022)

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Article Computer Science, Information Systems

A problem-specific non-dominated sorting genetic algorithm for supervised feature selection

Yu Zhou et al.

Summary: This paper proposes a problem-specific non-dominated sorting genetic algorithm (PS-NSGA) that can minimize three objectives of feature selection. By applying an accuracy-preferred domination operator and a quick bit mutation, the algorithm converges faster and better, achieving competitive classification accuracy in experiments.

INFORMATION SCIENCES (2021)

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Article Computer Science, Artificial Intelligence

An evolutionary multi-objective optimization framework of discretization-based feature selection for classification

Yu Zhou et al.

Summary: This paper proposes an evolutionary multi-objective optimization framework for discretization-based feature selection, introducing a flexible cut-point PSO algorithm to explore relevant features. By using multiple objective optimization and Pareto ensemble method, it aims to find the best feature subset effectively.

SWARM AND EVOLUTIONARY COMPUTATION (2021)

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Article Engineering, Electrical & Electronic