Enhanced Classification Models for Iris Dataset

Wu, Yuanyuan; He, Jing; Ji, Yimu; Huang, Guang-Li; Yao, Haichang; Zhang, Peng; Xu, Wen; Guo, Mengjiao; Li, Youtao

doi:10.1016/j.procs.2019.12.072

Cited by 12 publications

(7 citation statements)

References 8 publications

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“…Figure 1 shows the flowchart of the k-means algorithm using the exact weight solution method. e iris data set is a commonly used machine learning data set [33]. It includes four attributes, the length of the calyx (Speal Length), the width of the calyx (Speal Width), the length of the petal (Petal Length), and the width of the petal (Petal Width).…”

Section: K-means With the Accurate Weight Determinationmentioning

confidence: 99%

An Accurate Method of Determining Attribute Weights in Distance-Based Classification Algorithms

Liu

Wang

2022

Mathematical Problems in Engineering

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Weight determination aims to determine the importance of different attributes; determining accurate weights can significantly improve the accuracy of classification and clustering. This paper proposes an accurate method for attribute weight determination. The method uses the distance from the sample point of each class to the class center point. It can minimize the weights and determines the attribute weights of the constraints through the objective function. In this paper, the attribute weights obtained by the exact solution are applied to the K-means clustering algorithm; three classic machine learning data sets, the iris data set, the wine data set, and the wheat seed data set, are clustered. Using the normalized mutual information as the evaluation index, a confusion matrix was established. Finally, the clustering results are visualized and compared with other methods to verify the effectiveness of the proposed method. The results show that this method improves the normalized mutual information by 0.11 and 0.08, respectively, compared with the unweighted and entropy weighted methods for iris clustering results. Furthermore, the performance on the wine data set is improved by 0.1, and the performance on the wheat seed data set is improved by 0.15 and 0.05.

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Section: K-means With the Accurate Weight Determinationmentioning

confidence: 99%

An Accurate Method of Determining Attribute Weights in Distance-Based Classification Algorithms

Liu

Wang

2022

Mathematical Problems in Engineering

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“…These pretrained CNN models have different architectures (ie, different neural numbers, convolution sizes, and pooling modes), they show the different performances when capturing a specific feature (see Section 5). Inspired by the multiple strategies coupling algorithm and the Random Forests algorithm, we propose a combined classification model, which is designed to integrate the advantages of various models, that is, to select the best answer from these predicted outputs in each subcategory.…”

Section: Methodsmentioning

confidence: 99%

A combination model based on transfer learning for waste classification

Huang

et al. 2020

Concurrency and Computation

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Summary The increasing amount of solid waste is becoming a significant problem that needs to be addressed urgently. The reliable and accurate classification method is a crucial step in waste disposal because different types of wastes have different disposal ways. The existing waste classification models driven by deep learning are not easy to achieve accurate results and still need to be improved due to the various architecture networks adopted. Their performance on different datasets is varied, and there is also a lack of specific large‐scale datasets for training. We propose a new combination classification model based on three pretrained CNN models (VGG19, DenseNet169, and NASNetLarge) for processing the ImageNet database and achieve high classification accuracy. In our proposed model, the transfer learning model based on each pretrained model is constructed as a candidate classifier, and the optimal output of three candidate classifiers is selected as the final classification result. The experiments based on two waste image datasets demonstrate that the proposed model achieves 96.5% and 94% classification accuracy and outperforms several counterpart methods.

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“…The CASIA iris image dataset, version 1.0, contains 756 images from 108 different persons. There are seven images for each eye [25]. Each iris image has a resolution of 280×320 pixels and is in grayscale.…”

Section: Iris Image Datasetmentioning

confidence: 99%

Enhance iris segmentation method for person recognition based on image processing techniques

2023

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The limitation of traditional iris recognition systems to process iris images captured in unconstraint environments is a breakthrough. Automatic iris recognition has to face unpredictable variations of iris images in real-world applications. For example, the most challenging problems are related to the severe noise effects that are inherent to these unconstrained iris recognition systems, varying illumination, obstruction of the upper or lower eyelids, the eyelash overlap with the iris region, specular highlights on pupils which come from a spot of light during captured the image, and decentralization of iris image which caused by the person's gaze. Iris segmentation is one of the most important processes in iris recognition. Due to the different types of noise in the eye image, the segmentation result may be erroneous. To solve this problem, this paper develops an efficient iris segmentation algorithm using image processing techniques. Firstly, the outer boundary segmentation of the iris problem is solved. Then the pupil boundary is detected. Testes are done on the Chinese Academy of Sciences' Institute of Automation (CASIA) database. Experimental results indicate that the proposed algorithm is efficient and effective in terms of iris segmentation and reduction of time processing. The accuracy results for both datasets (CASIA-V1 and V4) are 100% and 99.16 respectively.

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Enhanced Classification Models for Iris Dataset

Cited by 12 publications

References 8 publications

An Accurate Method of Determining Attribute Weights in Distance-Based Classification Algorithms

An Accurate Method of Determining Attribute Weights in Distance-Based Classification Algorithms

A combination model based on transfer learning for waste classification

Enhance iris segmentation method for person recognition based on image processing techniques

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