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

Abstract

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.

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

“…This is due to many reasons, which can be summarized as follows; (1) NB can provide fast predictions rather than other classification algorithms because the training time has an order O(N) with the dataset, (2) it can be easily trained with small amount of input training dataset and it can be used also for large datasets as well, (3) the simplicity and easy implementation with the ability of real-time training for new items, (4) the implementation of this classifier has no required adjusting parameters or domain knowledge, (5) It handles both continuous and discrete data, (6) NB is less sensitive to missing data, (7) NB has high capability to handle the noise in the dataset, (8) NB is an Incremental learning approach because its functions work from an approximation of low-order probabilities which are extracted from the training data. Hence, these can be quickly updated as new training data are obtained, (9) If the Naive Bayes conditional independence assumption holds, then it will converge quicker than discriminative models like logistic regression, (10) NB can be used for both binary and multiclass classification problems and (11) NB is sufficient for real-time applications such as diseases diagnoses because it relies on a set of pre-computed probabilities that make the classification done in a very short time (Khotimah et al 2020;Kaur and Oberoi 2020), Although NB has proven efficiency with real-time applications, its performance is sometimes thumping in many cases because of the unrealistic assumption that all features have the same degree of importance and are independent of the given class value. Hence, this unrealistic assumption should be mitigated to overcome such hurdles.…”

Comparing Data Mining Algorithms for Breast Cancer Diagnosis

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“…Pada proses evolusi, sejumlah gen penyusun kromosom akan mengalami proses persilangan dan mutasi. Genetic Algorithm menggunakan transisi probabilistik untuk memilih kromosom terbaik untuk mendapatkan solusi yang optimal [23].…”