A Hybrid Feature Selection Approach for Microarray Gene Expression Data

Tan, Feng; Fu, Xuezheng; Wang, Hao; Zhang, Yanqing; Bourgeois, Anu G.

doi:10.1007/11758525_92

Cited by 16 publications

(11 citation statements)

References 7 publications

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“…Recently, several authors proposed hybrid approaches taking advantages of both filter and wrapper methods. Examples of hybrid algorithms include t-statistics and a GA [20], a correlation based feature selection algorithm and a genetic algorithm [21], principal component analysis and an ACO algorithm [22], chi-square approach and a multi-objective optimization algorithm [23], mutual information and a GA [24,25]. The idea behind the hybrid method is that filter methods are first applied to select a feature pool and then the wrapper method is applied to find the optimal subset of features from the selected feature pool.…”

Section: Related Workmentioning

confidence: 99%

Bi-level dimensionality reduction methods using feature selection and feature extraction

Veerabhadrappa¹,

Rangarajan²

2010

IJCA

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Variety of feature selection methods have been developed in the literature, which can be classified into three main categories: filter, wrapper and hybrid approaches. Filter methods apply an independent test without involving any learning algorithm, while wrapper methods require a predetermined learning algorithm for feature subset evaluation. Filter and wrapper methods have their drawbacks and are complementary to each other. The filter approaches have low computational cost with insufficient reliability in classification while wrapper methods tend to have superior classification accuracy but require great computational effort. The methods proposed in this paper are bi-level dimensionality reduction methods that integrate filter method and feature extraction method with the aim to improve the classification performance of the features selected. In the two approaches proposed, in level 1 of dimensionality reduction, feature are selected based on mutual correlation and in level 2 selected features are used to extract features using PCA or LPP. To evaluate the performance of the proposed methods several experiments are conducted on standard datasets and the results obtained show superiority of the proposed methods over single level dimensionality reduction techniques (feature selection based on Mutual correlation, PCA and LPP).

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Section: Related Workmentioning

confidence: 99%

Bi-level dimensionality reduction methods using feature selection and feature extraction

Veerabhadrappa¹,

Rangarajan²

2010

IJCA

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“…al [12] first apply fuzzy logic for pre-selecting attributes in microarray data, and then apply a genetic algorithm that uses a wrapper. The same idea of first building a pool of promising attributes and then applying a genetic algorithm to that pool is used in [27]. We consider a frequently used forward selection algorithm called Sequential Forward Selection (SFS).…”

Section: Related Work On Speeding Up the Wrappermentioning

confidence: 99%

Large-scale attribute selection using wrappers

Gütlein¹,

Frank

Hall³

et al. 2009

2009 IEEE Symposium on Computational Intelligence and Data Mining

200

114

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Abstract-Scheme-specific attribute selection with the wrapper and variants of forward selection is a popular attribute selection technique for classification that yields good results. However, it can run the risk of overfitting because of the extent of the search and the extensive use of internal cross-validation. Moreover, although wrapper evaluators tend to achieve superior accuracy compared to filters, they face a high computational cost. The problems of overfitting and high runtime occur in particular on high-dimensional datasets, like microarray data.We investigate Linear Forward Selection, a technique to reduce the number of attributes expansions in each forward selection step. Our experiments demonstrate that this approach is faster, finds smaller subsets and can even increase the accuracy compared to standard forward selection. We also investigate a variant that applies explicit subset size determination in forward selection to combat overfitting, where the search is forced to stop at a precomputed "optimal" subset size. We show that this technique reduces subset size while maintaining comparable accuracy.

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“…We also compare our algorithm against a hybrid of filter and wrap-79 per approaches-filter-wrapper (FW). Many hybrid algorithms have been proposed for feature subset selection with encouraging re-81 sults [24][25][26][27][28][29]. It was not possible to implement all the methods and empirically assess them.…”

Section: Comparative Performance Analysis 71mentioning

confidence: 99%

“…Examples of hybrid 87 algorithms include t-statistics and a GA [24], a correlation-based feature selection algorithm and a genetic algorithm [25], principal com-89 ponent analysis and an ACO algorithm [26], chi-square approach and a multi-objective optimization algorithm [27], mutual information 91 and a GA [28,29]. The idea behind the hybrid method is that filter methods are first applied to select a feature pool and then the wrap-93 per method is applied to find the optimal subset of features from the selected feature pool.…”

Section: Review Of Existing Techniques 19mentioning

confidence: 99%

“…Stage 1: Since, it is hard to decide which filter method is best 87 for a dataset because the performance of a filter method varies with different datasets [24], we use a number of popular filter methods 89 to filter out irrelevant features. FW eliminates a feature when all of these filter methods-t-test, symmetric uncertainty [38] …”

Section: Comparative Performance Analysis 71mentioning

confidence: 99%

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Feature subset selection in large dimensionality domains

Gheyas

Smith

2010

Pattern Recognition

515

231

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Searching for an optimal feature subset from a high dimensional feature space is known to be an NPcomplete problem. We present a hybrid algorithm, SAGA, for this task. SAGA combines the ability to avoid being trapped in a local minimum of simulated annealing with the very high rate of convergence of the crossover operator of genetic algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of generalized regression neural networks. We compare the performance over time of SAGA and well-known algorithms on synthetic and real datasets. The results show that SAGA outperforms existing algorithms.

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A Hybrid Feature Selection Approach for Microarray Gene Expression Data

Cited by 16 publications

References 7 publications

Bi-level dimensionality reduction methods using feature selection and feature extraction

Bi-level dimensionality reduction methods using feature selection and feature extraction

Large-scale attribute selection using wrappers

Feature subset selection in large dimensionality domains

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