A Hybrid PSO-SFS-SBS Algorithm in Feature Selection for Liver Cancer Data

Gunasundari, S.; Janakiraman, S.

doi:10.1007/978-81-322-2119-7_133

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Coveragebased methods include sequential forward feature selection (SFS), sequential backward feature selection (SBS), etc. For feature selection in our framework, we use the sequential FS algorithm, and the algorithm selects important features [16].…”

Section: Feature Selection Algorithmsmentioning

confidence: 99%

Sequential Feature Selection and Machine Learning Algorithm-Based Patient’s Death Events Prediction and Diagnosis in Heart Disease

Aggrawal

Pal

2020

SN COMPUT. SCI.

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Due to the accessibility of data with multiple features, many feature determination techniques available in written form. These features promote data with extremely high measurement values. The feature determination strategy provides us with a way to reduce calculation time, improve prediction execution, and have a better understanding of data in machine learning, as well as a way to recognize applications. As pointed out by related works that have been reviewed, in general, existing works only focus on amplifying classification accuracy. For real-world applications, the selected subset of features must be continuous instead. In this research, proposes a sequential feature selection algorithm for detecting death events in heart disease patients during treatment to select the most important features. Several machine learning algorithms (LDA, RF, GBC, DT, SVM, and KNN) are used. In addition, the accuracy obtained by this method (SFS) is compared with the accuracy of the classifier. The confusion matrix, ROC curve, precision, recall rate, and f1-score are also calculated to verify the results obtained by the SFS algorithm. The experimental results show that for Random Forest Classifier_FS, the SFS method reaches 86.67% accuracy.

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Section: Feature Selection Algorithmsmentioning

confidence: 99%

Sequential Feature Selection and Machine Learning Algorithm-Based Patient’s Death Events Prediction and Diagnosis in Heart Disease

Aggrawal

Pal

2020

SN COMPUT. SCI.

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“…Consequently, the hybrid feature selection algorithm combines a filter algorithm and a wrapper algorithm, which results in rapid calculation by the filter algorithm and the high predictive accuracy by the wrapper algorithm. Gunasundari et al [28] presented two types of hybridization, namely particle swarm optimization with sequential forward selection (PSO-SFS), and PSO with SFS and SBS (PSO-SFS-SBS) algorithms by combining the strengths of both the methods. MATLAB was used to classify liver disease as benign and malignant lesion from an abdominal CT.…”

Section: Related Workmentioning

confidence: 99%

“…The results of a hybrid PSO-SFS-SBS algorithm provided the best feature subset with 40% of features selected. Furthermore, the performance of classification gained the highest accuracy, i.e., 96.4 %, with 20 features for dataset-1 (85 features) and dataset-II (108 features) provides good accuracy, i.e., 92.6%, with 21 features [28]. Naqvi [22] presented a method involving a hybrid filter and wrapper.…”

Section: Related Workmentioning

confidence: 99%

Liver Cancer Classification Model Using Hybrid Feature Selection Based on Class-Dependent Technique for the Central Region of Thailand

Panthong

Srivihok

2019

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Liver cancer data always consist of a large number of multidimensional datasets. A dataset that has huge features and multiple classes may be irrelevant to the pattern classification in machine learning. Hence, feature selection improves the performance of the classification model to achieve maximum classification accuracy. The aims of the present study were to find the best feature subset and to evaluate the classification performance of the predictive model. This paper proposed a hybrid feature selection approach by combining information gain and sequential forward selection based on the class-dependent technique (IGSFS-CD) for the liver cancer classification model. Two different classifiers (decision tree and naïve Bayes) were used to evaluate feature subsets. The liver cancer datasets were obtained from the Cancer Hospital Thailand database. Three ensemble methods (ensemble classifiers, bagging, and AdaBoost) were applied to improve the performance of classification. The IGSFS-CD method provided good accuracy of 78.36% (sensitivity 0.7841 and specificity 0.9159) on LC_dataset-1. In addition, LC_dataset II delivered the best performance with an accuracy of 84.82% (sensitivity 0.8481 and specificity 0.9437). The IGSFS-CD method achieved better classification performance compared to the class-independent method. Furthermore, the best feature subset selection could help reduce the complexity of the predictive model.

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An improved fuzzy-differential evolution approach applied to classification of tumors in liver CT scan images

AmirHosseini

Hosseini

2019

Med Biol Eng Comput

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A Hybrid PSO-SFS-SBS Algorithm in Feature Selection for Liver Cancer Data

Cited by 5 publications

References 16 publications

Sequential Feature Selection and Machine Learning Algorithm-Based Patient’s Death Events Prediction and Diagnosis in Heart Disease

Sequential Feature Selection and Machine Learning Algorithm-Based Patient’s Death Events Prediction and Diagnosis in Heart Disease

Liver Cancer Classification Model Using Hybrid Feature Selection Based on Class-Dependent Technique for the Central Region of Thailand

An improved fuzzy-differential evolution approach applied to classification of tumors in liver CT scan images

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