Hybrid Framework Using Multiple-Filters and an Embedded Approach for an Efficient Selection and Classification of Microarray Data

Bonilla-Huerta, Edmundo; Hernandez-Montiel, Alberto; Morales-Caporal, Roberto; Arjona, M. A.

doi:10.1109/tcbb.2015.2474384

Cited by 54 publications

(30 citation statements)

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“…Therefore, future work can be supported by involving heuristic search instead of sequential search selection. If the work is extended involving ensemble [7] and embedded [2] methods; it may support with clear insight in complexity analysis and direction for further improvements.…”

Section: Resultsmentioning

confidence: 98%

Hybrid Feature Subset Selection Approach for Fuzzy-Extreme Learning Machine

Kale¹,

Sonavane²

2017

Data-Enabled Discov. Appl.

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Extreme learning machine (ELM) is a fast learning algorithm which has various advancements in terms of speed, reliability and generalized performance. Perhaps, it lacks the ability to solve the weighted classification problem. One of the alternative solutions to this problem is fuzzy-ELM (F-ELM) which combines the advantages of a fuzzy system and ELM. The major aim of this research is to handle the feature subset selection (FSS) and weighted classification problem. In this paper, a hybrid FSS approach for F-ELM classifier (H-FELM) is designed for binary and multiclass classification on the clinical dataset. Experimental results illustrate that H-FELM has the capability to handle these problems with improved classification accuracy by selecting prominent features which are relevant and nonredundant. The selection of such features can reduce the computational overhead and hence minimize the learning time. In order to validate the efficiency and effectiveness of the proposed algorithm, the comparative performance is evaluated by using four different strategies-1. existing F-ELM, 2. existing F-ELM with FSS, 3. ELM and F-ELM and 4. ELM with FSS and F-ELM with FSS. It is observed that the designed algorithm achieves an improvement of 9-10%

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Section: Resultsmentioning

confidence: 98%

Hybrid Feature Subset Selection Approach for Fuzzy-Extreme Learning Machine

Kale¹,

Sonavane²

2017

Data-Enabled Discov. Appl.

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“…Multiple filters combined with fusion approach are used in (Bonilla-Huerta et al. 2015 ) to select the initial subset of genes for GA feature selection approach. A new ensemble feature selection approach based on Sort Aggregation (SA) is proposed in (Wang et al.…”

Section: Related Workmentioning

confidence: 99%

A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification

Nagarajan

Babu

2021

Netw Model Anal Health Inform Bioinforma

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Feature selection has gained its importance due to the voluminous nature of the data. Owing to the computational complexity of wrapper approaches, the poor performance of filtering techniques, and the classifier dependency of embedded approaches, hybrid approaches are more commonly used in feature selection. Hybrid approaches use filtering metrics to reduce the computational complexity of wrapper algorithms and are proved to yield better feature subset. Though filtering metrics select the features based on their significance, most of them are unstable and biased towards the metric used. Moreover, the choice of filtering metrics depends largely on the distribution of data and data types. Biomedical datasets contain features with different distribution and types adding to the complexity in the choice of filtering metric. We address this problem by proposing a stable filtering method based on rank aggregation in hybrid feature selection model with Improved Squirrel search algorithm for biomedical datasets. Our proposed model is compared with other well-known and state-of-the-art methods and the results prove that our model exhibited superior performance in terms of classification accuracy and computational time. The robustness of our proposed model is proved by conducting experiments on nine biomedical datasets and with three different classifiers.

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“…Thus, selecting informative, interacting and related gene subset not only reduces computational time and effort, but also increases the accuracy of classification that reflects the efficiency process [7,57]. Moreover, most of the genes are redundant; to address this issue, feature selection methodology is implemented first to select and extract out a subset of small group of genes [45]. According to Saeys et al, feature selection techniques are broadly divided into three kinds in relation 1:3 to classification techniques, filter, wrapper and embedded methods [10][11].…”

Section: Background and Literature Reviewmentioning

confidence: 99%