English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Objectives: We have implemented a bio-inspired algorithm, particle swarm optimization method of miRNA subset selection to indentify the irreverent and redundant miRNAs for proper assessment of cancer diagnosis. Methods/Statistical Analysis: In this study we develop a creative multitier framework for subset selection to improve accuracy of caner classification. In the first tier we have used different filter methods to rank miRNAs according to their class relation then using union operator we have created a combinational model (Second tier) which consist of top ranked features of individual filter methods. Here the miRNAs are indentified according to their ranking with the threshold value defined. In third tier (feature pre selection model) improvised competitive swarm optimization algorithm is used to generate feasible optimal subset from the generated weighted miRNA in second tier to detect the biomarker gene for cancer detection. To minimize the gap between exploration and exploitation we have used Mamdani Fuzzy interference system. All selected genes from the fourth tire (feature reselection) is classify with classifier such as KNN. Findings: The objective has successfully achieved by implementing improvised competitive swarm optimization technique. Experimental result demonstrated that the proposed ICSO-KNN performs better than other method like PSO, PCA and PSO-KNN. ICSO-KNN outperformed with less error and larger amount of new solutions. Application/Improvements: We have four tier frameworks as an efficient feature selection algorithm which outperforms better. This approach may help to use any other metaheuristic feature selection to solve multimodal subset problem.

Multi-tier hybrid feature selection by combining filter and wrapper for subset feature selection in cancer classification