Distributed feature selection (DFS) strategy for microarray gene expression data to improve the classification performance

Potharaju, Sai Prasad; Sreedevi, M.

doi:10.1016/j.cegh.2018.04.001

Cited by 43 publications

(17 citation statements)

References 12 publications

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“…Sai Prasad et al implementedCurse of dimentionality is the most serious downside of data in microarray as it has more number of attributes (features) [13] . This leads to disheartened computational stability.…”

Section: Feature Relief Algorithm For Bio-informaticsmentioning

confidence: 99%

Cluster Optimization for Boundary Points using Distributive Progressive Feature Selection Algorithm

Ramesh¹

2021

TURCOMAT

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A group of different data objects is classified as similar objects is known as clusters. It is the process of finding homogeneous data items like patterns, documents etc. and then group the homogenous data items togetherothers groupsmay have dissimilar data items. Most of the clustering methods are either crisp or fuzzy and moreover member allocation to the respective clusters is strictly based on similarity measures and membership functions.Both of the methods have limitations in terms of membership. One strictly decides a sample must belong to single cluster and other anyway fuzzy i.e probability. Finally, Quality and Purity like measure are applied to understand how well clusters are created. But there is a grey area in between i.e. ‘Boundary Points’ and ‘Moderately Far’ points from the cluster centre. We considered the cluster quality [18], processing time and relevant features identification as basis for our problem statement and implemented Zone based clustering by using map reducer concept. I have implemented the process to find far points from different clusters and generate a new cluster, repeat the above process until cluster quantity is stabilized. By using this processwe can improve the cluster quality and processing time also.

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Section: Feature Relief Algorithm For Bio-informaticsmentioning

confidence: 99%

Cluster Optimization for Boundary Points using Distributive Progressive Feature Selection Algorithm

Ramesh¹

2021

TURCOMAT

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“…In 2019, Potharaju et al introduced a novel distributed feature selection method to remedy the curse-of-dimensionality of microarray data [33]. Their technique is inspired by an academic method of forming final year project groups.…”

Section: Feature Selection Algorithmsmentioning

confidence: 99%

Classification of Microarray Gene Expression Data Using an Infiltration Tactics Optimization (ITO) Algorithm

Zahoor

Zafar

2020

Genes

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A number of different feature selection and classification techniques have been proposed in literature including parameter-free and parameter-based algorithms. The former are quick but may result in local maxima while the latter use dataset-specific parameter-tuning for higher accuracy. However, higher accuracy may not necessarily mean higher reliability of the model. Thus, generalized optimization is still a challenge open for further research. This paper presents a warzone inspired “infiltration tactics” based optimization algorithm (ITO)—not to be confused with the ITO algorithm based on the Itõ Process in the field of Stochastic calculus. The proposed ITO algorithm combines parameter-free and parameter-based classifiers to produce a high-accuracy-high-reliability (HAHR) binary classifier. The algorithm produces results in two phases: (i) Lightweight Infantry Group (LIG) converges quickly to find non-local maxima and produces comparable results (i.e., 70 to 88% accuracy) (ii) Followup Team (FT) uses advanced tuning to enhance the baseline performance (i.e., 75 to 99%). Every soldier of the ITO army is a base model with its own independently chosen Subset selection method, pre-processing, and validation methods and classifier. The successful soldiers are combined through heterogeneous ensembles for optimal results. The proposed approach addresses a data scarcity problem, is flexible to the choice of heterogeneous base classifiers, and is able to produce HAHR models comparable to the established MAQC-II results.

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“…The training time and testing time taken by various algorithms are calculated in terms of seconds. It is observed from the table that, for almost all data sets, the execution time by the proposed method is much less than that of the existing [40] 91.18 90 100 BDE-XRankf [29] 82.4 75 95 8-S PMSO [33] 98.1 94.2 -IRLDA [41] 97 --GEM [25] 91.5 91.2 -AEN-CMI [37] 91.05 89.30 -SLR [38] 95.51 94.61 -DFS [59] 98 The bold fonts indicate the highest results and the name of the proposed techniques. methods.…”

Section: Runtime Analysismentioning

confidence: 99%

Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

Baliarsingh

Vipsita

2020

IET syst. biol.

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Distributed feature selection (DFS) strategy for microarray gene expression data to improve the classification performance

Cited by 43 publications

References 12 publications

Cluster Optimization for Boundary Points using Distributive Progressive Feature Selection Algorithm

Cluster Optimization for Boundary Points using Distributive Progressive Feature Selection Algorithm

Classification of Microarray Gene Expression Data Using an Infiltration Tactics Optimization (ITO) Algorithm

Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

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