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

Alia, Ahmed; Taweel, Adel

doi:10.5815/ijitcs.2017.04.08

Cited by 13 publications

(15 citation statements)

References 37 publications

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“…In their studies, the original features were ranked by both methods and the top ranked attributes were selected as the more relevant ones by using the feature relevance [32]. In another study, Alia and Taweel developed a new algorithm for Feature Selection based on hybrid Binary Cuckoo Search and rough set theory for classification on nominal datasets [33]. In this study, the SS method [34] which is beneficial in order to gain knowledge of data and identify germane features was used.…”

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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“…A binary version of the algorithm is presented in Rodrigues et al (2013). Cuckoo search algorithm has been used for feature subset selection in Kulshestha et al (2015), Pereira et al (2014) and Alia & Taweel (2017).…”

Section: Cuckoo Searchmentioning

confidence: 99%

A new penalty-based wrapper fitness function for feature subset selection with evolutionary algorithms

Chakraborty

Kawamura

2018

Journal of Information and Telecommunication

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Feature subset selection is an important preprocessing task for any real life data mining or pattern recognition problem. Evolutionary computational (EC) algorithms are popular as a search algorithm for feature subset selection. With the classification accuracy as the fitness function, the EC algorithms end up with feature subsets having considerably high recognition accuracy but the number of residual features also remain quite high. For high dimensional data, reduction of number of features is also very important to minimize computational cost of overall classification process. In this work, a wrapper fitness function composed of classification accuracy with another penalty term which penalizes for large number of features has been proposed. The proposed wrapper fitness function is used for feature subset evaluation and subsequent selection of optimal feature subset with several EC algorithms. The simulation experiments are done with several benchmark data sets having small to large number of features. The simulation results show that the proposed wrapper fitness function is efficient in reducing the number of features in the final selected feature subset without significant reduction of classification accuracy. The proposed fitness function has been shown to perform well for high-dimensional data sets with dimension up to 10,000.

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“…The minimum subset of features from the original data is obtained by removing the redundant and irrelevant features [9]. There are many studies on this subject in the literature.…”

Section: A Feature Selectionmentioning

confidence: 99%

A Study on Diagnosis of Parkinson’s Disease from Voice Dysphonias

Akyol¹

2018

IJITCS

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

Cited by 13 publications

References 37 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

A new penalty-based wrapper fitness function for feature subset selection with evolutionary algorithms

A Study on Diagnosis of Parkinson’s Disease from Voice Dysphonias

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