A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection

Maleki, Negar; Zeinali, Yasser; Niaki, Seyed Taghi Akhavan

doi:10.1016/j.eswa.2020.113981

Cited by 193 publications

(104 citation statements)

References 18 publications

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“…During this subsection, the proposed Weighted Naïve Bayes Module (WNBM) will be evaluated. WNBM is compared against the most recently used classification methods which are; (i) Enhanced K-Nearest Neighbors (EKNN) [52] , (ii) Naïve Bayes-Probabilistic Kernel Classifier (NB-PKC) [53] , and (iii) Whale Optimization Algorithm-SVM (WOA-SVM) [54] . Results are shown in table 15 and table 16 .…”

Section: Resultsmentioning

confidence: 99%

Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy

Shaban¹,

Rabie

Saleh

et al. 2021

Pattern Recognition

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COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Early detection of COVID-19 patients is an important issue for treating and controlling the disease from spreading. In this paper, a new strategy for detecting COVID-19 infected patients will be introduced, which is called Distance Biased Naïve Bayes (DBNB). The novelty of DBNB as a proposed classification strategy is concentrated in two contributions. The first is a new feature selection technique called Advanced Particle Swarm Optimization (APSO) which elects the most informative and significant features for diagnosing COVID-19 patients. APSO is a hybrid method based on both filter and wrapper methods to provide accurate and significant features for the next classification phase. The considered features are extracted from Laboratory findings for different cases of people, some of whom are COVID-19 infected while some are not. APSO consists of two sequential feature selection stages, namely; Initial Selection Stage (IS 2 ) and Final Selection Stage (FS 2 ). IS 2 uses filter technique to quickly select the most important features for diagnosing COVID-19 patients while removing the redundant and ineffective ones. This behavior minimizes the computational cost in FS 2 , which is the next stage of APSO. FS 2 uses Binary Particle Swarm Optimization (BPSO) as a wrapper method for accurate feature selection. The second contribution of this paper is a new classification model, which combines evidence from statistical and distance based classification models. The proposed classification technique avoids the problems of the traditional NB and consists of two modules; Weighted Naïve Bayes Module (WNBM) and Distance Reinforcement Module (DRM). The proposed DBNB tries to accurately detect infected patients with the minimum time penalty based on the most effective features selected by APSO. DBNB has been compared with recent COVID-19 diagnose strategies. Experimental results have shown that DBNB outperforms recent COVID-19 diagnose strategies as it introduce the maximum accuracy with the minimum time penalty.

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Section: Resultsmentioning

confidence: 99%

Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy

Shaban¹,

Rabie

Saleh

et al. 2021

Pattern Recognition

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“…In [68], a k-Nearest-Neighbors technique, for which a genetic algorithm is utilized for the efficient feature selection to decrease the dataset dimensions and improve the classification accuracy, is employed for diagnosing the stage of patients' disease. Moreover, in [69] a new two-layer feature selection approach that combines a wrapper and an embedded method in constructing an appropriate subset of predictors is proposed.…”

Section: Related Workmentioning

confidence: 99%

A novel community detection based genetic algorithm for feature selection

2021

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The feature selection is an essential data preprocessing stage in data mining. The core principle of feature selection seems to be to pick a subset of possible features by excluding features with almost no predictive information as well as highly associated redundant features. In the past several years, a variety of meta-heuristic methods were introduced to eliminate redundant and irrelevant features as much as possible from high-dimensional datasets. Among the main disadvantages of present meta-heuristic based approaches is that they are often neglecting the correlation between a set of selected features. In this article, for the purpose of feature selection, the authors propose a genetic algorithm based on community detection, which functions in three steps. The feature similarities are calculated in the first step. The features are classified by community detection algorithms into clusters throughout the second step. In the third step, features are picked by a genetic algorithm with a new community-based repair operation. Nine benchmark classification problems were analyzed in terms of the performance of the presented approach. Also, the authors have compared the efficiency of the proposed approach with the findings from four available algorithms for feature selection. Comparing the performance of the proposed method with three new feature selection methods based on PSO, ACO, and ABC algorithms on three classifiers showed that the accuracy of the proposed method is on average 0.52% higher than the PSO, 1.20% higher than ACO, and 1.57 higher than the ABC algorithm.

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“…As a metaheuristic search strategy, the GA is mainly used to find the best super parameter algorithm of machine learning [45]. Generally speaking, the genetic algorithm is divided into five stages [46]:…”

Section: Genetic Algorithm (Ga) Ga Was First Proposed By Johnmentioning

confidence: 99%

Classification of Abnormal Traffic in Smart Grids Based on GACNN and Data Statistical Analysis

Zhang

et al. 2021

Security and Communication Networks

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With the continuous development of smart grids, communication networks carry more and more power services, and at the same time, they are also facing more and more security issues. For example, some malicious software usually uses encryption technology or tunnel technology to bypass firewalls, intrusion detection systems, etc., thereby posing a serious threat to the information security of smart grids. At present, the classification of network traffic mainly depends on the correct extraction of network protocol characteristics. However, the process of extracting network features by some traditional methods is time-consuming and overly dependent on experience. In order to solve the problem of accurate classification of power network traffic, this paper proposes a method of convolutional neural network based on genetic algorithm optimization (GACNN) and data statistical analysis. This method can simultaneously extract the time characteristics between different packet groups and the spatial characteristics in the same packet group. Therefore, it greatly saves manpower and gets rid of the dependence on experience value. The proposed method has been tested and verified on the UNSW-NB15 dataset and the real dataset collected by the power company. The results show that the proposed method can correctly classify abnormal network flows and is much better than traditional machine learning methods. In large-scale real network flow scenarios, the detection rate of the proposed method exceeds 97%, while the traditional method is generally less than 90%.

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A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection

Cited by 193 publications

References 18 publications

Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy

Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy

A novel community detection based genetic algorithm for feature selection

Classification of Abnormal Traffic in Smart Grids Based on GACNN and Data Statistical Analysis

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