Feature selection using swarm-based relative reduct technique for fetal heart rate

Inbarani, H. Hannah; Banu, P. K. Nizar; Azar, Ahmad Taher

doi:10.1007/s00521-014-1552-x

Cited by 53 publications

(11 citation statements)

References 45 publications

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“…It is multi-mapping items for inadequate information about the genuine characteristic [18][19][20][21][22][23][24][25][26][27][28]. Hybrid rough set systems have been used in different applications for feature selections [29][30][31][32][33][34], classifications [35][36][37][38][46][47][48] and image segmentation [39]. Jaganathan et al [40] suggested amount of feature significance based on fuzzy entropy, experienced with a radial basis function network classifier for classification using five UCI healthcare benchmark data sets.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Covering-based rough set classification system

Kumar

Inbarani

Azar

et al. 2016

Neural Comput & Applic

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Medical data classification is applied in intelligent medical decision support system to classify diseases into different categories. Several classification methods are commonly used in various healthcare settings. These techniques are fit for enhancing the nature of prediction, initial identification of sicknesses and disease classification. The categorization complexities in healthcare area are focused around the consequence of healthcare data investigation or depiction of medicine by the healthcare professions. This study concentrates on applying uncertainty (i.e. rough set)-based pattern classification techniques for UCI healthcare data for the diagnosis of diseases from different patients. In this study, covering-based rough set classification (i.e. proposed pattern classification approach) is applied for UCI healthcare data. Proposed CRS gives effective results than delicate pattern classifier model. The results of applying the CRS classification method to UCI healthcare data analysis are based upon a variety of disease diagnoses. The execution of the proposed covering-based rough set classification is contrasted with other approaches, such as rough set (RS)-based classification methods, Kth nearest neighbour, improved bijective soft set, support vector machine, modified soft rough set and back propagation neural network methodologies using different evaluating measures.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Covering-based rough set classification system

Kumar

Inbarani

Azar

et al. 2016

Neural Comput & Applic

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“…Fourthly, feature extraction based on Genetic Algorithm (GA) used as proposed in Xu et al (2014) and as for classification linear regression, linear Support Vector Machine and kernel radial basis function (RBF) SVM were utilized. Moreover, as presented in Inbarani, Banu & Azar (2014), in order to identify the most important features, unsupervised swarm-based reduction techniques, hybrids of swarm intelligence and rough sets were employed and to check the level of errors generated from the reduce set by applying K-means clustering, and for classification, this study used single decision tree (DT), multilayer perceptron (MLP) neural network, probabilistic neural network (PNN) and random forest. Finally, as proposed in Kim, Yang & Lee (2017), it used fitMine as a new nonlinear dynamic model that reflected the relationship between signals of Fetal Heart Rate and Uterine Contraction by combining chaotic population model and unscented Kalman filter algorithm.…”

Section: Techniques and Algorithmsmentioning

confidence: 99%

“…From the articles that we reviewed, we found that researchers were motivated to develop and use automated methods to diagnose, feature extraction and classification of cardiotocography focused on improving early detection and rapid diagnosis, accuracy, guidelines, data sets, ONG experts, methods and techniques. This section describes the Haweel & Bangash (2013), Gavrilis, Nikolakopoulos & Georgoulas (2015), Shah et al (2015), Chamidah & Wasito (2015), Cömert, Kocamaz & Güngör (2016), Georgoulas et al (2017), Permanasari & Nurlayli (2017), Nagendra et al (2017), Zhang & Zhao (2017), Sahin & Subasi (2015), Ocak (2013), Ocak & Ertunc (2013), Yılmaz (2016), Chinnasamy, Muthusamy & Gopal (2013), Inbarani, Banu & Azar (2014), Sundar, Chitradevi & Geetharamani (2014) Publicly available Each record provides information about morphological patterns (physiological, suspect, pathological) Gavrilis, Nikolakopoulos & Georgoulas (2015), Shah et al (2015), Chamidah & Wasito (2015), Jyothi, Hiwale & Bhat (2016), Magenes et al (2016), Warmerdam et al (2016), Cömert & Kocamaz (2017b), Zhang & Zhao (2017), Georgieva et al (2013), Xu et al (2014), Yılmaz (2016), Cömert & Kocamaz (2017a), Kim, Yang & Lee (2017),…”

Section: Motivationmentioning

confidence: 99%

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A systematic review of automated pre-processing, feature extraction and classification of cardiotocography

Al-Yousif

Jaenul²,

Al-Dayyeni

et al. 2021

PeerJ Computer Science

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Context The interpretations of cardiotocography (CTG) tracings are indeed vital to monitor fetal well-being both during pregnancy and childbirth. Currently, many studies are focusing on feature extraction and CTG classification using computer vision approach in determining the most accurate diagnosis as well as monitoring the fetal well-being during pregnancy. Additionally, a fetal monitoring system would be able to perform detection and precise quantification of fetal heart rate patterns. Objective This study aimed to perform a systematic review to describe the achievements made by the researchers, summarizing findings that have been found by previous researchers in feature extraction and CTG classification, to determine criteria and evaluation methods to the taxonomies of the proposed literature in the CTG field and to distinguish aspects from relevant research in the field of CTG. Methods Article search was done systematically using three databases: IEEE Xplore digital library, Science Direct, and Web of Science over a period of 5 years. The literature in the medical sciences and engineering was included in the search selection to provide a broader understanding for researchers. Results After screening 372 articles, and based on our protocol of exclusion and inclusion criteria, for the final set of articles, 50 articles were obtained. The research literature taxonomy was divided into four stages. The first stage discussed the proposed method which presented steps and algorithms in the pre-processing stage, feature extraction and classification as well as their use in CTG (20/50 papers). The second stage included the development of a system specifically on automatic feature extraction and CTG classification (7/50 papers). The third stage consisted of reviews and survey articles on automatic feature extraction and CTG classification (3/50 papers). The last stage discussed evaluation and comparative studies to determine the best method for extracting and classifying features with comparisons based on a set of criteria (20/50 articles). Discussion This study focused more on literature compared to techniques or methods. Also, this study conducts research and identification of various types of datasets used in surveys from publicly available, private, and commercial datasets. To analyze the results, researchers evaluated independent datasets using different techniques. Conclusions This systematic review contributes to understand and have insight into the relevant research in the field of CTG by surveying and classifying pertinent research efforts. This review will help to address the current research opportunities, problems and challenges, motivations, recommendations related to feature extraction and CTG classification, as well as the measurement of various performance and various data sets used by other researchers.

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“…Hannah Inbarani et.al [14] proposed an unsupervised feature selection method based on particle swarm optimization with relative reduct. Relative reduct is based on rough set theory.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised feature selection using binary bat algorithm

Rani¹,

Rajalaxmi

2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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Feature selection is selecting a subset of optimal features. Feature selection is being used in high dimensional data reduction and it is being used in several applications like medical, image processing, text mining, etc. Several methods were introduced for unsupervised feature selection. Among those methods some are based on filter approach and some are based on wrapper approach. In the existing work, unsupervised feature selection methods using Genetic Algorithm, Particle Swarm Optimization with Relative Reduct, Quick Reduct and Ant Colony Optimization have been introduced. These methods yield better performance for unsupervised feature selection. In this paper we proposed a novel method to select subset of features from unlabeled data using binary bat algorithm with sum of squared error as the fitness function. The proposed method is then tested with various classification algorithms like decision tree, multilayer perceptron, support vector machine and clustering quality measures like sum of squared error. The results show that our proposed method gives more accuracy when compared with other optimization algorithm. Index Terms-Unsupervised feature selection, Binary bat algorithm, K -means

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Feature selection using swarm-based relative reduct technique for fetal heart rate

Cited by 53 publications

References 45 publications

RETRACTED ARTICLE: Covering-based rough set classification system

RETRACTED ARTICLE: Covering-based rough set classification system

A systematic review of automated pre-processing, feature extraction and classification of cardiotocography

Unsupervised feature selection using binary bat algorithm

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