A hybrid model of fuzzy min–max and brain storm optimization for feature selection and data classification

Pourpanah, Farhad; Lim, Chee Peng; Wang, Xizhao; Tan, Choo Jun; Seera, Manjeevan; Shi, Yuhui

doi:10.1016/j.neucom.2019.01.011

Cited by 54 publications

(28 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, a dynamic clustering number strategy is used to converge the algorithm to an optimal solution quickly. The authors in [18] improve the performance by integrating the fuzzy min-max neural network with BSO. The feature extraction on a subset of data improves predictive accuracy and reduces the search space.…”

Section: A Brain Storm Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Clusters Adaptive Brain Storm Optimization Algorithm for QoS-Aware Service Composition

Peng

Wang

2020

IEEE Access

View full text Add to dashboard Cite

Service composition provides an effective means to fulfill users' personalized requirements and complex business applications. With the number of web services rapidly growing, finding the best combination among services based on quality of service (QoS) poses a critical computational challenge due to the exponential growth of alternative composite services. Some efforts are developed to find the near-optimal service combination within an acceptable time range. They fall into two categories of solutions: exploring partial combinations in the service space and downsizing the optimization problem in scale. Although they solve the scalability problem to some extent, the required computational time is usually high. A promising direction is to integrate these two categories of solutions. However, its practical application suffers from three challenges: no good search scheme, no consideration of the natural organization of sub-problems, and the lack of diverse combinations. In this work, we propose a novel approach, called multi-clusters adaptive brain storm optimization (MCaBSO) algorithm. The proposed method uses brain storm optimization (BSO) as the search scheme to combine the division of search space with the exploration of the reduced search space. MCaBSO uses the twin support vector machine (TWSVM) to effectively divide the search space according to the natural organization of sub-problems. MCaBSO provides an adaptive dual strategy that gives guidance for the generation of diverse combinations. MCaBSO enables the agile exploration of the reduced search space and generates more high-quality combinations. MCaBSO is evaluated on two datasets to show effectiveness and efficiency.

show abstract

Section: A Brain Storm Optimizationmentioning

confidence: 99%

“…p intra = 1 − p inter (18) where p low and p high are two constants that are used to determine the proportions of using inter-cluster operator and intra-cluster operator, respectively. N Cgen and N Mgen are the current and maximum iteration numbers.…”

Section: ) Adaptive Dual Strategymentioning

confidence: 99%

Multi-Clusters Adaptive Brain Storm Optimization Algorithm for QoS-Aware Service Composition

Peng

Wang

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The technique is balanced by adjusting the loudness and pulse emission rate as follows: . from the previous equations, it will be noted that bats follow the frequency change, which works to accelerate their access to the best solution [6] .…”

Section: Bat Algorithmmentioning

confidence: 99%

“…This paper is divided into two stages of work, where the irst stage begins using fuzzy in order to determine the features that affect the work results and then use the MI mutual information technology that enters only important data in the research process [5]. The second stage is to use the BBA binary bat algorithm [6].…”

Section: Introductionmentioning

confidence: 99%

Classification of diseases using a hybrid fuzzy mutual information technique with binary bat algorithm

AL-Taie¹,

Qasim²

2020

Ann Proteom Bioinform

View full text Add to dashboard Cite

Genetic datasets have a large number of features that may signifi cantly affect the disease classifi cation process, especially datasets related to cancer diseases. Evolutionary algorithms (EA) are used to fi nd the fastest and best way to perform these calculations, such as the bat algorithm (BA) by reducing the dimensions of the search area after changing it from continuous to discrete. In this paper, a method of gene selection was proposed two sequent stages: in the fi rst stage, the fuzzy mutual information (FMI) method is used to choose the most important genes selected through a fuzzy model that was built based on the dataset size. In the second stage, the BBA is used to reduce and determine a fi xed number of genes affecting the process of classifi cation, which came from the fi rst stage. The proposed algorithm, FMI_BBA, describes effi ciency, by obtaining a higher classifi cation accuracy and a few numbers of selected genes compared to other algorithms.How to cite this article: Yonis AL-Taie FA, Qasim OS. Classifi cation of diseases using a hybrid fuzzy mutual information technique with binary bat algorithm. Ann Proteom Bioinform. 2020; 4: 001-005.

show abstract

“…With the recent advancement in digital technologies, the size of data sets has become too large in which traditional data processing and machine learning techniques are not able to cope with effectively [1,2]. However, analyzing complex, high dimensional, and noise-contaminated data sets is a huge challenge, and it is crucial to develop novel algorithms that are able to summarize, classify, extract important information and convert them into an understandable form [3][4][5].…”

mentioning

confidence: 99%

Recent advances in deep learning

Wang

Zhao

Pourpanah

2020

Int. J. Mach. Learn. & Cyber.

Self Cite

200

View full text Add to dashboard Cite

A hybrid model of fuzzy min–max and brain storm optimization for feature selection and data classification

Cited by 54 publications

References 48 publications

Multi-Clusters Adaptive Brain Storm Optimization Algorithm for QoS-Aware Service Composition

Multi-Clusters Adaptive Brain Storm Optimization Algorithm for QoS-Aware Service Composition

Classification of diseases using a hybrid fuzzy mutual information technique with binary bat algorithm

Recent advances in deep learning

Contact Info

Product

Resources

About