Weight Optimize by Automatic Unsupervised Clustering using Computation Intelligence

Lowongtrakool, C.; Hiransakolwong, Nualsawat

doi:10.5120/7930-1261

Cited by 4 publications

(9 citation statements)

References 19 publications

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“…In image segmentation, clustering algorithms are very popular as they are intuitive and are also easy to implement. The [22][27] K-means clustering algorithm is one of the most widely used algorithm in the literature, and many authors successfully compare their new proposal with the results achieved by the k-Means. Amarintrarak N, R. Sailkean K., Tongsima S. Wiwatwattana N, [12] proposed a method for automatic detection of root crowns in root images, are designed, implemented and quantitatively compared.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Image segmentation and classification are important tools to provide the information about the Dermoscopic images clinically in terms of its size and shape. Chaloemchai Lowongtrakool and Nualsawat Hiransakolwong [22], their paper proposes AUCCI, the Design of Image Segmentation Using Automatic Unsupervised Clustering Computation Intelligence, a novel automatic image clustering algorithm based on Computation Intelligence. V. Senthil, R. Bhaskaran, [24] talk about analyzes the robustness of watermarking method in still images using Haar, Daubecheies and Biorthogonal wavelets.…”

Section: Noteworthy Contributions In the Field Of Proposed Workmentioning

confidence: 99%

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A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach

Jaiswal¹

2013

IOSR-JCE

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In image analysis, segmentation is the partitioning of a digital image into multiple regions (sets of pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in literature and there are a wide variety of approaches that are used. Different approaches are suited to different types of images and the quality of output of a particular algorithm is difficult to measure quantitatively due to the fact that there may be much correct segmentation for a single image. Image segmentation denotes a process by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest domain-independent abstraction of an input image. Image segmentation is an important processing step in many image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular image or set of images, or more generally, for a whole class of images. In this paper, The Survey of Image Segmentation using Artificial Intelligence and Evolutionary Approach methods that have been proposed in the literature. The rest of the paper is organized as follows. 1. Introduction, 2.Literature review, 3.Noteworthy contributions in the field of proposed work, 4.Proposed Methodology, 5.Expected outcome of the proposed research work, 6.Conclusion.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Noteworthy Contributions In the Field Of Proposed Workmentioning

confidence: 99%

A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach

Jaiswal¹

2013

IOSR-JCE

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“…The Pima Indian Diabetes (PID) dataset from the University of California, Irvine (UCI) Repository of Machine Learning database is used to train and evaluate machine learning algorithms. The irrelevant data (noise) will influence the decision of the algorithm to be used [4]. One of the algorithms used is the K-means algorithm which is used to categorize and classify patients into healthy (non-diabetic) and diabetic categories.…”

Section: Introductionmentioning

confidence: 99%

Performa Comparison of the K-Means Method for Classification in Diabetes Patients Using Two Normalization Methods

Westari¹

2021

ijmra

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The diabetes classification system is very useful in the health sector. This paper discusses the classification system for diabetes using the K-Means algorithm. The Pima Indian Diabetes (PID) dataset is used to train and evaluate this algorithm. The unbalanced value range in the attributes affects the quality of the classification result, so it is necessary to preprocess the data which is expected to improve the accuracy of the PID dataset classification result. Two types of preprocessing methods are used that are min-max normalization and z-score normalization. These two normalization methods are used and the classification accuracies are compared. Before the data classification process is carried out, the data is divided into training data and test data. The result of the classification test using the K-Means algorithm has shown that the best accuracy lies in the PID dataset which has been normalized using the min-max normalization method, which 79% compared to z-score normalization.

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“…It is possible that a noise detection technique performs better than another technique in a certain area or on a definite type of noise. The noise elimination is generally a difficult task, although, in machine learning, it results in obtaining accurate models and high performance (Lowongtrakool and Hiransakolwong, 2012). Developing robust and non-sensitive-to-noise classifiers can also prevent the degradation of the performance of the classifier.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, in regard to leading the system to make a decision about the use of noisy data, in numerous recently proposed techniques, learning the raw data has remained a very challenging issue. In case noisy data are not removed, the system fails to detect noisy samples, which may result in making wrong decisions (Lowongtrakool and Hiransakolwong, 2012). Data from the real world are hardly ever arranged in definite categories, and they suffer from a number of limitations, e.g.…”

Section: Introductionmentioning

confidence: 99%

The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection

Nematzadeh

Ibrahim

Selamat

et al. 2020

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Purpose The purpose of this study is to enhance data quality and overall accuracy and improve certainty by reducing the negative impacts of the FCM algorithm while clustering real-world data and also decreasing the inherent noise in data sets. Design/methodology/approach The present study proposed a new effective model based on fuzzy C-means (FCM), ensemble filtering (ENS) and machine learning algorithms, called an FCM-ENS model. This model is mainly composed of three parts: noise detection, noise filtering and noise classification. Findings The performance of the proposed model was tested by conducting experiments on six data sets from the UCI repository. As shown by the obtained results, the proposed noise detection model very effectively detected the class noise and enhanced performance in case the identified class noisy instances were removed. Originality/value To the best of the authors’ knowledge, no effort has been made to improve the FCM algorithm in relation to class noise detection issues. Thus, the novelty of existing research is combining the FCM algorithm as a noise detection technique with ENS to reduce the negative effect of inherent noise and increase data quality and accuracy.

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Weight Optimize by Automatic Unsupervised Clustering using Computation Intelligence

Cited by 4 publications

References 19 publications

A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach

A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach

Performa Comparison of the K-Means Method for Classification in Diabetes Patients Using Two Normalization Methods

The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection

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