Integration of multi-objective PSO based feature selection and node centrality for medical datasets

Rostami, Mehrdad; Forouzandeh, Saman; Berahmand, Kamal; Soltani, Mina

doi:10.1016/j.ygeno.2020.07.027

Cited by 147 publications

(63 citation statements)

References 80 publications

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“…The reported results showed that the use of these mechanisms greatly increased the search ability of particle optimization algorithms for highdimensional datasets. Moreover, in [31], a novel graph-based feature selection method is developed to increase disease diagnosis accuracy. In this method, using the node centrality criterion, a new mechanism for initializing the particles is proposed.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

“…Techniques of optimization based on the population including ant colony optimization (ACO) [28], genetic algorithm (GA) [21], simulated annealing (SA) [29], taboo search (TS) [30], and particle swarm optimization (PSO) [31] were recently used in feature selection. In fact, hybrid search strategies have been used that merge the wrapper and filter approaches.…”

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A novel community detection based genetic algorithm for feature selection

2021

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“…High dimensional DNA microarray has presented serious challenges to the existing machine learning and classification methods. In other words, in many of medical and microarray datasets, it is possible that many genes are irrelevant or redundant for machine learning algorithm [29][30][31][32]. Feature selection or gene selection is a popular and powerful approach in medical datasets to overcome this shortcoming [33][34][35].…”

Section: -3 Feature Selectionmentioning

confidence: 99%

Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis

Ataei

Majidi

Zahiri

et al. 2021

Preprint

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Most of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naïve Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.

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“…Dimensionality reduction is used to reduce time complexity and space complexity, saving the cost of observing all features and making the system robust by using the most relevant features of the dataset [6]. Feature selection is one of the dimensionality reduction methods that help to choose the most relevant terms before applying the learning algorithm [7]. Feature selection methods can be divided mainly into three categories: filter, wrapper, and embedded [5].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Selection for Amharic News Document Classification

Endalie

Haile

2021

Mathematical Problems in Engineering

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Today, the amount of Amharic digital documents has grown rapidly. Because of this, automatic text classification is extremely important. Proper selection of features has a crucial role in the accuracy of classification and computational time. When the initial feature set is considerably larger, it is important to pick the right features. In this paper, we present a hybrid feature selection method, called IGCHIDF, which consists of information gain (IG), chi-square (CHI), and document frequency (DF) features’ selection methods. We evaluate the proposed feature selection method on two datasets: dataset 1 containing 9 news categories and dataset 2 containing 13 news categories. Our experimental results showed that the proposed method performs better than other methods on both datasets 1and 2. The IGCHIDF method’s classification accuracy is up to 3.96% higher than the IG method, up to 11.16% higher than CHI, and 7.3% higher than DF on dataset 2, respectively.

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Integration of multi-objective PSO based feature selection and node centrality for medical datasets

Cited by 147 publications

References 80 publications

A novel community detection based genetic algorithm for feature selection

A novel community detection based genetic algorithm for feature selection

Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis

Hybrid Feature Selection for Amharic News Document Classification

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