Comparing Multiobjective Evolutionary Algorithms for Cancer Data Microarray Feature Selection

Nowadays women are commonly diagnosed with breast cancer. Feature based Selection method plays an important step while constructing a classification based framework. We have proposed Multi filter union (MFU) feature selection method for breast cancer data set. The feature selection process based on random forest algorithm and Logistic regression (LG) algorithm based union model is used for selecting important features in the dataset. The performance of the data analysis is evaluated using optimal features subset from selected dataset. The experiments are computed with data set of Wisconsin diagnostic breast cancer center and next the real data set from women health care center. The result of the proposed approach shows high performance and efficient when comparing with existing feature selection algorithms.

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A Novel Hybrid Feature Selection and Ensemble Learning Framework for Unbalanced Cancer Data Diagnosis with Transcriptome and Functional Proteomic

IEEE Access ◽

10.1109/access.2021.3070428 ◽

2021 ◽

pp. 1-1

Author(s):

Xianfang Tang ◽

Lijun Cai ◽

Yajie Meng ◽

Changlong Gu ◽

Jialiang Yang ◽

...

Keyword(s):

Feature Selection ◽

Ensemble Learning ◽

Cancer Data ◽

Learning Framework

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An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Complexity ◽

10.1155/2018/3051854 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 7

Author(s):

Jing Xiao ◽

Jing-Jing Li ◽

Xi-Xi Hong ◽

Min-Mei Huang ◽

Xiao-Min Hu ◽

...

Keyword(s):

Genetic Algorithm ◽

Evolutionary Algorithms ◽

Portfolio Optimization ◽

Optimization Problem ◽

Experimental Comparison ◽

Software Project ◽

Project Portfolio ◽

Multiobjective Evolutionary Algorithms ◽

Reference Distance ◽

Nondominated Sorting

As it is becoming extremely competitive in software industry, large software companies have to select their project portfolio to gain maximum return with limited resources under many constraints. Project portfolio optimization using multiobjective evolutionary algorithms is promising because they can provide solutions on the Pareto-optimal front that are difficult to be obtained by manual approaches. In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. MOEA/D_RD replaces solutions based on reference distance during evolution process. Experimental comparison and analysis are performed among MOEA/D_RD and several state-of-the-art multiobjective evolutionary algorithms, that is, MOEA/D, nondominated sorting genetic algorithm II (NSGA2), and nondominated sorting genetic algorithm III (NSGA3). The results show that MOEA/D_RD and NSGA2 can solve the software project portfolio optimization problem more effectively. For 4-objective optimization problem, MOEA/D_RD is the most efficient algorithm compared with MOEA/D, NSGA2, and NSGA3 in terms of coverage, distribution, and stability of solutions.

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