Neural network ensembles for image identification using Pareto-optimal features

Albukhanajer, Wissam A.; Jin, Yaochu; Briffa, Johann A.

doi:10.1109/cec.2014.6900349

Cited by 8 publications

(6 citation statements)

References 20 publications

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“…Multi‐objective ensemble generation can not only be applied to classification or regression problems, but also to other machine learning problems such as feature selection or extraction. Albukhanajer et al proposed a method for image identification, where a set of Pareto optimal image features are used as ensemble inputs to glean ensemble diversity. The Pareto optimal image features are obtained by minimizing the within‐class variance and maximizing between‐class variance of the features extracted from images.…”

Section: Discussion and Challengesmentioning

confidence: 99%

Multi‐objective ensemble generation

Cheng

Jin

2015

WIREs Data Min & Knowl

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Ensemble methods that combine a committee of machine-learning models, each known as a member or base learner, have gained research interests in the past decade. One interest on ensemble generation involves the multi-objective approach, which attempts to generate both accurate and diverse members that fulfill the theoretical requirements of good ensembles. These methods resolve common difficulties of balancing the trade-off between accuracy and diversity and have been shown to be advantageous over single-objective methods. This study presents an up-to-date survey on multi-objective ensemble generation methods, including widely used diversity measures, member generation, selection, and integration techniques. Challenges and potential applications of multi-objective ensemble generation are also discussed.

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Section: Discussion and Challengesmentioning

confidence: 99%

Multi‐objective ensemble generation

Cheng

Jin

2015

WIREs Data Min & Knowl

Self Cite

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“…To obtain the final clustering result, a majority voting strategy is implemented on the nondominated solutions to select the training samples. In practical application, the combination of knee-based approach and ensemble-based approach for recurrent neural networks is successfully used in the prediction of computational fluid dynamic simulations [39] and image identification [1]. In these literatures, not all the individuals in the Pareto set are considered as suitable solutions, only the Pareto-optimal solutions around the knee-point are employed to implement the ensemble task.…”

Section: The Multiobjective Evolutionary Algorithm Based Clusteringmentioning

confidence: 99%

“…The proposed algorithm PESC takes advantage of the superiority of MOEA, which can generate a set of solutions, to find the possible nonzero entries of the similarity matrix. Assume that each individual can find one possible nonzero entry for each sample, the objective functions can be formulated as formulas (1) and 2:…”

Section: Mathematical Description Of Pescmentioning

confidence: 99%

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A pareto ensemble based spectral clustering framework

Luo

Zhou³

2020

Complex Intell. Syst.

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Similarity matrix has a significant effect on the performance of the spectral clustering, and how to determine the neighborhood in the similarity matrix effectively is one of its main difficulties. In this paper, a “divide and conquer” strategy is proposed to model the similarity matrix construction task by adopting Multiobjective evolutionary algorithm (MOEA). The whole procedure is divided into two phases, phase I aims to determine the nonzero entries of the similarity matrix, and Phase II aims to determine the value of the nonzero entries of the similarity matrix. In phase I, the main contribution is that we model the task as a biobjective dynamic optimization problem, which optimizes the diversity and the similarity at the same time. It makes each individual determine one nonzero entry for each sample, and the encoding length decreases to O(N) in contrast with the non-ensemble multiobjective spectral clustering. In addition, a specific initialization operator and diversity preservation strategy are proposed during this phase. In phase II, three ensemble strategies are designed to determine the value of the nonzero value of the similarity matrix. Furthermore, this Pareto ensemble framework is extended to semi-supervised clustering by transforming the semi-supervised information to constraints. In contrast with the previous multiobjective evolutionary-based spectral clustering algorithms, the proposed Pareto ensemble-based framework makes a balance between time cost and the clustering accuracy, which is demonstrated in the experiments section.

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“…I MAGE classification is an important task in machine learning and computer vision with a wide range of applications, e.g., face images, hyperspectral images, medical images, and vehicle images [1,2,3,4]. Image classification is the task of assigning class labels to images according to the content in the images.…”

Section: Introductionmentioning

confidence: 99%

Genetic Programming-Based Discriminative Feature Learning for Low-Quality Image Classification

Bi¹,

Xue²,

Zhang³

2021

Preprint

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Being able to learn discriminative features from low-quality images has raised much attention recently due to their wide applications ranging from autonomous driving to safety surveillance. However, this task is difficult due to high variations across images, such as scale, rotation, illumination, and viewpoint, and distortions in images, such as blur, low contrast, and noise. Image preprocessing could improve the quality of the images, but it often requires human intervention and domain knowledge. Genetic programming (GP) with a flexible representation can automatically perform image preprocessing and feature extraction without human intervention. Therefore, this study proposes a new evolutionary learning approach using GP (EFLGP) to learn discriminative features from images with blur, low contrast, and noise for classification. In the proposed approach, we develop a new program structure (individual representation), a new function set, and a new terminal set. With these new designs, EFLGP can detect small regions from a large input low-quality image, select image operators to process the regions or detect features from the small regions, and output a flexible number of discriminative features. A set of commonly used image preprocessing operators is employed as functions in EFLGP to allow it to search for solutions that can effectively handle low-quality image data. The performance of EFLGP is comprehensively investigated on eight datasets of varying difficulty under the original (clean), blur, low contrast, and noise scenarios, and compared with a large number of benchmark methods using handcrafted features and deep features. The experimental results show that EFLGP achieves significantly better or similar results in most comparisons. The results also reveal that EFLGP is more invariant than the benchmark methods to blur, low contrast, and noise.

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Neural network ensembles for image identification using Pareto-optimal features

Cited by 8 publications

References 20 publications

Multi‐objective ensemble generation

Multi‐objective ensemble generation

A pareto ensemble based spectral clustering framework

Genetic Programming-Based Discriminative Feature Learning for Low-Quality Image Classification

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