Feature Selection using a Novel Particle Swarm Optimization and It’s Variants

Parimala, R.; Nallaswamy, R.

doi:10.5815/ijitcs.2012.05.03

Cited by 10 publications

(12 citation statements)

References 19 publications

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“…Chittineni and Bhogapathi applied the Exhaustive search and Heuristic search techniques in order to determine features that contribute to cluster data [30]. Parimala and Nallaswamy proposed swarm optimization technique, binary particle swarm optimization technique and its variants in order to select the optimal feature subset [31]. Kalpana and Mani compared the two methods Median Based Discretization and ChiMerge discretization.…”

Section: Proposed Approach and Experimental Resultsmentioning

confidence: 99%

A Study on Liver Disease Diagnosis based on Assessing the Importance of Attributes

Akyol¹,

Gültepe²

2017

IJISA

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Abstract-Liver is a needful body organ that forms an important barrier between the gastrointestinal blood, which contains large amounts of toxins, and antigens. Liver diseases contain hepatitis B and hepatitis C virus infections, alcoholic liver disease, nonalcoholic fatty liver disease and associated cirrhosis, liver failure and hepatocellular carcinoma are primary causes of death. The main purpose of this study is to investigate which attributes are important for effective diagnosis of liver disorders by performing the machine learning approach based on the combination of Stability Selection and Random Forest methods. In order to generate more accuracy, dataset was balanced by utilizing the Random Under-Sampling method. Important ones in all attributes were detected by utilizing the Stability Selection method which was performed on sub-datasets, which were obtained with 5 fold cross-validation technique. By sending these datasets to the Random Forest algorithm, the performance of the proposed approach was evaluated within the frame of accuracy and sensitive metrics. The experimental results clearly show that the Random Under-Sampling method can potentially improve the performance of the combination of Stability Selection and Random Forest methods in machine learning. And, the combination of these methods provides new perspectives for the diagnosis of this disease and other medical diseases.

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Section: Proposed Approach and Experimental Resultsmentioning

confidence: 99%

A Study on Liver Disease Diagnosis based on Assessing the Importance of Attributes

Akyol¹,

Gültepe²

2017

IJISA

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show abstract

“…For example, Chittineni and Bhogapathi applied the Exhaustive search and Heuristic search techniques in order to determine features that contribute to cluster data [7]. Parimala and Nallaswamy proposed swarm optimization technique, binary particle swarm optimization technique and its variants in order to select the optimal feature subset [8]. Kalpana and Mani compared the two methods Median Based Discretization and ChiMerge discretization.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Importance of Attributes for Diagnosis of Diabetes Disease

Akyol¹

2017

IJIEEB

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Abstract-Diabetes is a chronic, metabolic disease related to the rise of levels of blood glucose. According to the current data from the World Health Organization, 422 million adults have diabetes in the world and prevalence of diabetes is 13.2%. Disregarding the diagnosis and treatment of the disease leads to some major problems on kidneys, heart and blood vessels, eyes, nerves, pregnancy and wound healing. The most common type of diabetes and usually in adults, Type 2 diabetes occurs when the body becomes resistant to insulin or does not make enough insulin. The main objective of this study is to make more successful this disease by investigating the important attributes based on assessing the importance of attributes using the Stability Selection method. The proposed method might be a powerful tool for the importance of attributes, and effective diagnosis of this disease with the classification accuracy is 78.57% and ROC value is 0.75.

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“…They process multiple candidate solutions concurrently, and they are developed based on characteristics of biological systems [13]. They are widely used for improving the search strategy in FS, because they are easy to implement and incorporate mechanisms to avoid getting trapped in local optima [54] [59] [61] [65]. These algorithms are also able to find best/optimal solution in a reasonable time with efficient II.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection based on Hybrid Binary Cuckoo Search and Rough Set Theory in Classification for Nominal Datasets

Alia¹,

Taweel²

2017

IJITCS

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Abstract-Feature Selection (FS) is an important process to find the minimal subset of features from the original data by removing the redundant and irrelevant features. It aims to improve the efficiency of classification algorithms. Rough set theory (RST) is one of the effective approaches to feature selection, but it uses complete search to search for all subsets of features and dependency to evaluate these subsets. However, the complete search is expensive and may not be feasible for large data due to its high cost. Therefore, meta-heuristics algorithms, especially Nature Inspired Algorithms, have been widely used to replace the reduction part in RST. This paper develops a new algorithm for Feature Selection based on hybrid Binary Cuckoo Search and rough set theory for classification on nominal datasets. The developed algorithm is evaluated on five nominal datasets from the UCI repository, against a number of similar NIAs algorithms. The results show that our algorithm achieves better FS compared to two known NIAs in a lesser number of iterations, without significantly reducing the classification accuracy.

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Feature Selection using a Novel Particle Swarm Optimization and It’s Variants

Cited by 10 publications

References 19 publications

A Study on Liver Disease Diagnosis based on Assessing the Importance of Attributes

A Study on Liver Disease Diagnosis based on Assessing the Importance of Attributes

Assessing the Importance of Attributes for Diagnosis of Diabetes Disease

Feature Selection based on Hybrid Binary Cuckoo Search and Rough Set Theory in Classification for Nominal Datasets

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