scholarly journals SVM and KNN Based SGO Feature Selection Algorithm for Breast Cancer Diagnosis

2020 ◽  
Vol 8 (2S7) ◽  
pp. 2237-2240
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Subset Selection ◽  
Machine Learning Algorithms ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

In diagnosis and prediction systems, algorithms working on datasets with a high number of dimensions tend to take more time than those with fewer dimensions. Feature subset selection algorithms enhance the efficiency of Machine Learning algorithms in prediction problems by selecting a subset of the total features and thus pruning redundancy and noise. In this article, such a feature subset selection method is proposed and implemented to diagnose breast cancer using Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms. This feature selection algorithm is based on Social Group Optimization (SGO) an evolutionary algorithm. Higher accuracy in diagnosing breast cancer is achieved using our proposed model when compared to other feature selection-based Machine Learning algorithms

scholarly journals Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Tanya Gera ◽  
Jaiteg Singh ◽  
Abolfazl Mehbodniya ◽  
Julian L. Webber ◽  
Mohammad Shabaz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Hybrid Approach ◽  
Machine Learning Algorithms ◽  
Dynamic Monitoring ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Detection Techniques ◽  
Dominant Feature

Ransomware is a special malware designed to extort money in return for unlocking the device and personal data files. Smartphone users store their personal as well as official data on these devices. Ransomware attackers found it bewitching for their financial benefits. The financial losses due to ransomware attacks are increasing rapidly. Recent studies witness that out of 87% reported cyber-attacks, 41% are due to ransomware attacks. The inability of application-signature-based solutions to detect unknown malware has inspired many researchers to build automated classification models using machine learning algorithms. Advanced malware is capable of delaying malicious actions on sensing the emulated environment and hence posing a challenge to dynamic monitoring of applications also. Existing hybrid approaches utilize a variety of features combination for detection and analysis. The rapidly changing nature and distribution strategies are possible reasons behind the deteriorated performance of primitive ransomware detection techniques. The limitations of existing studies include ambiguity in selecting the features set. Increasing the feature set may lead to freedom of adept attackers against learning algorithms. In this work, we intend to propose a hybrid approach to identify and mitigate Android ransomware. This study employs a novel dominant feature selection algorithm to extract the dominant feature set. The experimental results show that our proposed model can differentiate between clean and ransomware with improved precision. Our proposed hybrid solution confirms an accuracy of 99.85% with zero false positives while considering 60 prominent features. Further, it also justifies the feature selection algorithm used. The comparison of the proposed method with the existing frameworks indicates its better performance.

scholarly journals An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional Dataset

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Senthilkumar Devaraj ◽  
S. Paulraj
Keyword(s):  
Feature Selection ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Complex Nature ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Multidimensional Datasets ◽  
Study Results

Multidimensional medical data classification has recently received increased attention by researchers working on machine learning and data mining. In multidimensional dataset (MDD) each instance is associated with multiple class values. Due to its complex nature, feature selection and classifier built from the MDD are typically more expensive or time-consuming. Therefore, we need a robust feature selection technique for selecting the optimum single subset of the features of the MDD for further analysis or to design a classifier. In this paper, an efficient feature selection algorithm is proposed for the classification of MDD. The proposed multidimensional feature subset selection (MFSS) algorithm yields a unique feature subset for further analysis or to build a classifier and there is a computational advantage on MDD compared with the existing feature selection algorithms. The proposed work is applied to benchmark multidimensional datasets. The number of features was reduced to 3% minimum and 30% maximum by using the proposed MFSS. In conclusion, the study results show that MFSS is an efficient feature selection algorithm without affecting the classification accuracy even for the reduced number of features. Also the proposed MFSS algorithm is suitable for both problem transformation and algorithm adaptation and it has great potentials in those applications generating multidimensional datasets.

scholarly journals A Hybrid Feature Selection Method for Improve the Accuracy of Medical Classification Process

2021 ◽  
Vol 11 (1) ◽  
pp. 50-55
Author(s):  
Maria Mohammad Yousef ◽  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Fitness Function ◽  
Machine Learning Algorithms ◽  
Feature Subset Selection ◽  
High Dimensionality ◽  
Support Vector ◽  
Feature Subset

Generally, medical dataset classification has become one of the biggest problems in data mining research. Every database has a given number of features but it is observed that some of these features can be redundant and can be harmful as well as disrupt the process of classification and this problem is known as a high dimensionality problem. Dimensionality reduction in data preprocessing is critical for increasing the performance of machine learning algorithms. Besides the contribution of feature subset selection in dimensionality reduction gives a significant improvement in classification accuracy. In this paper, we proposed a new hybrid feature selection approach based on (GA assisted by KNN) to deal with issues of high dimensionality in biomedical data classification. The proposed method first applies the combination between GA and KNN for feature selection to find the optimal subset of features where the classification accuracy of the k-Nearest Neighbor (kNN) method is used as the fitness function for GA. After selecting the best-suggested subset of features, Support Vector Machine (SVM) are used as the classifiers. The proposed method experiments on five medical datasets of the UCI Machine Learning Repository. It is noted that the suggested technique performs admirably on these databases, achieving higher classification accuracy while using fewer features.

Computational Method for Identifying Malonylation Sites by Using Random Forest Algorithm

2020 ◽  
Vol 23 (4) ◽  
pp. 304-312
Author(s):  
ShaoPeng Wang ◽  
JiaRui Li ◽  
Xijun Sun ◽  
Yu-Hang Zhang ◽  
Tao Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Learning Algorithms ◽  
Selection Procedure ◽  
Machine Learning Algorithms ◽  
Computational Method ◽  
Feature Subset ◽  
Random Forest Algorithm ◽  
Post Translational Modification

Background: As a newly uncovered post-translational modification on the ε-amino group of lysine residue, protein malonylation was found to be involved in metabolic pathways and certain diseases. Apart from experimental approaches, several computational methods based on machine learning algorithms were recently proposed to predict malonylation sites. However, previous methods failed to address imbalanced data sizes between positive and negative samples. Objective: In this study, we identified the significant features of malonylation sites in a novel computational method which applied machine learning algorithms and balanced data sizes by applying synthetic minority over-sampling technique. Method: Four types of features, namely, amino acid (AA) composition, position-specific scoring matrix (PSSM), AA factor, and disorder were used to encode residues in protein segments. Then, a two-step feature selection procedure including maximum relevance minimum redundancy and incremental feature selection, together with random forest algorithm, was performed on the constructed hybrid feature vector. Results: An optimal classifier was built from the optimal feature subset, which featured an F1-measure of 0.356. Feature analysis was performed on several selected important features. Conclusion: Results showed that certain types of PSSM and disorder features may be closely associated with malonylation of lysine residues. Our study contributes to the development of computational approaches for predicting malonyllysine and provides insights into molecular mechanism of malonylation.

Intelligent Feature Subset Selection with Machine Learning based Risk Management for DAS Prediction

2021 ◽  
pp. 08-16
Author(s):  
Mohamed Abdel Abdel-Basset ◽  
◽  
◽  
Mohamed Elhoseny
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Feature Selection ◽  
Subset Selection ◽  
Classification Problem ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Time Machine ◽  
Primary Level ◽  
Stage Process

In the current epidemic situations, people are facing several mental disorders related to Depression, Anxiety, and Stress (DAS). Numerous scales are developed for computing the levels for DAS, and DAS-21 is one among them. At the same time, machine learning (ML) models are applied widely to resolve the classification problem efficiently, and feature selection (FS) approaches can be designed to improve the classifier results. In this aspect, this paper develops an intelligent feature selection with ML-based risk management (IFSML-RM) for DAS prediction. The IFSML-RM technique follows a two-stage process: quantum elephant herd optimization-based FS (QEHO-FS) and decision tree (DT) based classification. The QEHO algorithm utilizes the input data to select a valuable subset of features at the primary level. Then, the chosen features are fed into the DT classifier to determine the existence or non-existence of DAS. A detailed experimentation process is carried out on the benchmark dataset, and the experimental results showcased the betterment of the IFSML-RM technique in terms of different performance measures.

scholarly journals BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm

2021 ◽  
Vol 7 ◽  
pp. e390
Author(s):  
Shafaq Abbas ◽  
Zunera Jalil ◽  
Abdul Rehman Javed ◽  
Iqra Batool ◽  
Mohammad Zubair Khan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Experimental Results ◽  
Support Vector ◽  
Novel Approach ◽  
Whale Optimization

Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy.

scholarly journals Machine Learning Based Supervised Feature Selection Algorithm for Data Mining

2019 ◽  
Vol 8 (10) ◽  
pp. 3396-3401 ◽  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Learning Algorithm ◽  
Modern World ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Minimum Number ◽  
Preprocessing Technique

Data Scientists focus on high dimensional data to predict and reveal some interesting patterns as well as most useful information to the modern world. Feature Selection is a preprocessing technique which improves the accuracy and efficiency of mining algorithms. There exist a numerous feature selection algorithms. Most of the algorithms failed to give better mining results as the scale increases. In this paper, feature selection for supervised algorithms in data mining are considered and given an overview of existing machine learning algorithm for supervised feature selection. This paper introduces an enhanced supervised feature selection algorithm which selects the best feature subset by eliminating irrelevant features using distance correlation and redundant features using symmetric uncertainty. The experimental results show that the proposed algorithm provides better classification accuracy and selects minimum number of features.

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