Gene Selection and Classification Rule Generation for Microarray Dataset

2013 ◽  
pp. 73-83 ◽  
Author(s):  
Soumen Kumar Pati ◽  
Asit Kumar Das
Keyword(s):  
Gene Selection ◽  
Microarray Dataset ◽  
Classification Rule ◽  
Rule Generation

Methods for Gene Selection and Classification of Microarray Dataset

2018 ◽  
pp. 66-77
Author(s):  
Mekour Norreddine
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Information Gain ◽  
Microarray Dataset ◽  
Data Sets ◽  
Expression Data ◽  
Wrapper Approach ◽  
Filter Approach

One of the problems that gene expression data resolved is feature selection. There is an important process for choosing which features are important for prediction; there are two general approaches for feature selection: filter approach and wrapper approach. In this chapter, the authors combine the filter approach with method ranked information gain and wrapper approach with a searching method of the genetic algorithm. The authors evaluate their approach on two data sets of gene expression data: Leukemia, and the Central Nervous System. The classifier Decision tree (C4.5) is used for improving the classification performance.

An Enhanced Recursive Firefly Algorithm for Informative Gene Selection

2019 ◽  
Vol 10 (2) ◽  
pp. 21-33 ◽  
Author(s):  
Nassima Dif ◽  
Zakaria Elberrichi
Keyword(s):  
Firefly Algorithm ◽  
Gene Selection ◽  
Optimal Solution ◽  
Microarray Dataset ◽  
Learning Task ◽  
Svm Classifier ◽  
Performance Score ◽  
Original Dataset ◽  
Wrapper Approach ◽  
Microarray Datasets

Feature selection is the process of identifying good performing combinations of significant features among many possibilities. This preprocess improves the classification accuracy and facilitates the learning task. For this optimization problem, the authors have used a metaheuristics approach. Their main objective is to propose an enhanced version of the firefly algorithm as a wrapper approach by adding a recursive behavior to improve the search of the optimal solution. They applied SVM classifier to investigate the proposed method. For the authors experimentations, they have used the benchmark microarray datasets. The results show that the new enhanced recursive FA (RFA) outperforms the standard version with a reduction of dimensionality for all the datasets. As an example, for the leukemia microarray dataset, they have a perfect performance score of 100% with only 18 informative selected genes among the 7,129 of the original dataset. The RFA was competitive compared to other state-of-art approaches and achieved the best results for CNS, Ovarian cancer, MLL, prostate, Leukemia_4c, and lymphoma datasets.

scholarly journals A New Approach for Optimization Classification Rule Generation Technique

2015 ◽  
Vol 118 (25) ◽  
pp. 28-32
Author(s):  
Leena Joshi ◽  
C.S.Satsangi C.S.Satsangi
Keyword(s):  
Classification Rule ◽  
Rule Generation ◽  
New Approach ◽  
Generation Technique

IHAC: Incorporating Heuristics for Efficient Rule Generation & Rule Selection in Associative Classification

2021 ◽  
Vol 20 (01) ◽  
pp. 2150010
Author(s):  
Parashu Ram Pal ◽  
Pankaj Pathak ◽  
Shkurte Luma-Osmani
Keyword(s):  
Association Rules ◽  
Search Space ◽  
Classification Rule ◽  
Rule Generation ◽  
Classification Methods ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Selection ◽  
Speed Up ◽  
Experimental Findings

Associations rule mining along with classification rule mining are both significant techniques of mining of knowledge in the area of knowledge discovery in massive databases stored in different geographic locations of the world. Based on such combination of these two, class association rules for mining or associative classification methods have been generated, which, in far too many cases, showed higher prediction accuracy than platitudinous conventional classifiers. Motivated by the study, in this paper, we proposed a new approach, namely IHAC (Incorporating Heuristics for efficient rule generation & rule selection in Associative Classification). First, it utilises the database to decrease the search space and then explicitly explores the potent class association rules from the optimised database. This also blends rule generation and classifier building to speed up the overall classifier construction cycle. Experimental findings showed that IHAC performs better than any further associative classification methods.

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