Improving Floating Search Feature Selection using Genetic Algorithm

Homsapaya, Kanyanut; Sornil, Ohm

doi:10.5614/itbj.ict.res.appl.2017.11.3.6

Cited by 6 publications

(7 citation statements)

References 24 publications

(23 reference statements)

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“…The accuracy of imputation with the hybrid method increased by 1%, showing better performance than the three other imputation models and without imputation. Using GA classifiers such as NB, k-NN and NN on the dataset repository produced significant differences in classification accuracy [22]. The hybrid method showed better classification accuracy with large-dimension datasets, with more than 90% classification accuracy at missing rate below 30%.…”

Section: Resultsmentioning

confidence: 99%

“…However, NB has very sensitive weaknesses in the selection of features, so it weighting and independent variable selection are required to improve model accuracy [21]. The genetic algorithm is an iterative method to get a global optimum for the selection of the features to be used as input for the naïve Bayes process [22][23][24][25][26][27][28]. This research used SOMI combined with feature selection by an evolutionary algorithm and NBC.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Feature Selection Using Genetic Algorithm in Naïve Bayes Classification for Incomplete Data

Khotimah¹,

Miswanto²,

Suprajitno³

2020

IJIES

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In the case of high dimensional data with missing values, the process of collecting data from various sources may be miss accidentally, which affected the quality of learning outcomes. a large number of machine learning methods can be applied to explore the search area for imputation and selection of features and parameters. ML classification needs preprocessing with self-organizing map imputation (SOMI) before the imputation of missing values is done to improve the accuracy of the model. This study introduces a new approach that combines naïve Bayes classification (NBC) and genetic algorithm (GA) optimization procedures to effectively explore the search space based on a sample of experimental points. GA is a classification model approach based on the selection of features that cause computational problems, such as reduced dimensions, uncertainty and imbalanced data sets with various classes. In the experiment, preprocessing the data using SOMI yielded error results that were up to 10% for various data sets with missing data compared to other methods. In the SOMI-GANB hybrid model, the experimental results show that the proposed method can significantly improve accuracy by up to 90% compared to other imputation methods and without feature selection. SOMI can be used for homogeneous, heterogeneous and mixed data sets. The results from the experiment clearly showed that the proposed method could significantly increase the yield compared to the other imputation methods and without feature selection. The combination of GA and naïve Bayes classification was chosen because they are simple, easy-to-understand methods that are very effective in finding optimal solutions from a set of possible solutions. Naïve Bayes imputation had higher accuracy compared to neural network imputation.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Feature Selection Using Genetic Algorithm in Naïve Bayes Classification for Incomplete Data

Khotimah¹,

Miswanto²,

Suprajitno³

2020

IJIES

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“…Instead, we propose a simpler approach, namely, floating search, which has obtained successful results in other research fields, such as feature selection [13][14][15][16].…”

Section: Methodsmentioning

confidence: 99%

Floating Search Methodology for Combining Classification Models for Site Recognition in DNA Sequences

Pérez-Rodríguez

Haro-García

García-Pedrajas

2021

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Recognition of the functional sites of genes, such as translation initiation sites, donor and acceptor splice sites and stop codons, is a relevant part of many current problems in bioinformatics. Recognition of the functional sites of genes is also a fundamental step in gene structure predictions in the most powerful programs. The best approaches to this type of recognition use sophisticated classifiers, such as support vector machines. However, with the rapid accumulation of sequence data, methods for combining many sources of evidence are necessary as it is unlikely that a single classifier can solve this type of problem with the best possible performance. A major issue is that the number of possible models to combine is large and the use of all of these models is impractical. In this paper, we present a framework that is based on floating search for combining as many classifiers as needed for the recognition of any functional sites of a gene. The methodology can be used for the recognition of translation initiation sites, donor and acceptor splice sites and stop codons. Furthermore, we can combine any number of classifiers that are trained on any species. The method is also scalable to large datasets, as is shown in experiments in which the whole human genome is used. The method is also applicable to other recognition tasks. We present experiments on the recognition of these four functional sites in the human genome, which is used as the target genome, and use another 20 species as sources of evidence. The proposed methodology shows significant improvement over state-of-the-art methods for use in a thorough evaluation process. The proposed method is also able to improve heuristic selection of species to be used as sources of evidence as the search finds the most useful datasets.

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“…A recent study from Homsapaya & Sornil in [6] introduced a floating search technique employing a genetic algorithm (GA) to improve the quality of the selected feature subset. The results showed that GA improved the performance for the majority of sample datasets.…”

Section: Feature Selectionmentioning

confidence: 99%

Adaptive Multi-level Backward Tracking for Sequential Feature Selection

Chotchantarakun

Sornil

2021

J. ICT Res. Appl.

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In the past few decades, the large amount of available data has become a major challenge in data mining and machine learning. Feature selection is a significant preprocessing step for selecting the most informative features by removing irrelevant and redundant features, especially for large datasets. These selected features play an important role in information searching and enhancing the performance of machine learning models. In this research, we propose a new technique called One-level Forward Multi-level Backward Selection (OFMB). The proposed algorithm consists of two phases. The first phase aims to create preliminarily selected subsets. The second phase provides an improvement on the previous result by an adaptive multi-level backward searching technique. Hence, the idea is to apply an improvement step during the feature addition and an adaptive search method on the backtracking step. We have tested our algorithm on twelve standard UCI datasets based on k-nearest neighbor and naive Bayes classifiers. Their accuracy was then compared with some popular methods. OFMB showed better results than the other sequential forward searching techniques for most of the tested datasets.

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Improving Floating Search Feature Selection using Genetic Algorithm

Cited by 6 publications

References 24 publications

Optimization of Feature Selection Using Genetic Algorithm in Naïve Bayes Classification for Incomplete Data

Optimization of Feature Selection Using Genetic Algorithm in Naïve Bayes Classification for Incomplete Data

Floating Search Methodology for Combining Classification Models for Site Recognition in DNA Sequences

Adaptive Multi-level Backward Tracking for Sequential Feature Selection

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