Feature Clustering for Pso-Based Feature Construction on High-Dimensional Data

Swesi, Idheba Mohamad Ali O.; Bakar, Azuraliza Abu

doi:10.32890/jict2019.18.4.3

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…In contrast, a correlation coefficient is a measurement tool used to look for relationship patterns between different characteristics [28]. Therefore, a bivariate study measures the degree of association between two variables and the direction of the relationship [29].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Data Feature Weighting Using Correlation Coefficients and Min–Max Normalization

Shantal,

Othman,

Bakar

2023

Symmetry

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In the realm of data analysis and machine learning, achieving an optimal balance of feature importance, known as feature weighting, plays a pivotal role, especially when considering the nuanced interplay between the symmetry of data distribution and the need to assign differential weights to individual features. Also, avoiding the dominance of large-scale traits is essential in data preparation. This step makes choosing an effective normalization approach one of the most challenging aspects of machine learning. In addition to normalization, feature weighting is another strategy to deal with the importance of the different features. One of the strategies to measure the dependency of features is the correlation coefficient. The correlation between features shows the relationship strength between the features. The integration of the normalization method with feature weighting in data transformation for classification has not been extensively studied. The goal is to improve the accuracy of classification methods by striking a balance between the normalization step and assigning greater importance to features with a strong relation to the class feature. To achieve this, we combine Min–Max normalization and weight the features by increasing their values based on their correlation coefficients with the class feature. This paper presents a proposed Correlation Coefficient with Min–Max Weighted (CCMMW) approach. The data being normalized depends on their correlation with the class feature. Logistic regression, support vector machine, k-nearest neighbor, neural network, and naive Bayesian classifiers were used to evaluate the proposed method. Twenty UCI Machine Learning Repository and Kaggle datasets with numerical values were also used in this study. The empirical results showed that the proposed CCMMW significantly improves the classification performance through support vector machine, logistic regression, and neural network classifiers in most datasets.

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Section: Introductionmentioning

confidence: 99%

A Novel Approach for Data Feature Weighting Using Correlation Coefficients and Min–Max Normalization

Shantal,

Othman,

Bakar

2023

Symmetry

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show abstract

“…On the other hand, the correlation coefficient is a statistical method that provides information on the strength and direction of the relationship between two variables [3]. It is pivotal in many works, particularly in data mining [4], such as feature selection [5][6][7] and missing data imputation methods [8][9][10][11]. However, missing data can be an issue in finding the correlation coefficient, as complete pairs are needed to calculate it.…”

Section: Introductionmentioning

confidence: 99%

Impact of Missing Data on Correlation Coefficient Values: Deletion and Imputation Methods for Data Preparation

Shantal,

Othman,

Abu Bakar

2023

Mal. J. Fund. Appl. Sci.

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The correlation coefficient is one of the essential statistical techniques used to discover relationships among variables. Various techniques can quantify correlation, such as Pearson's, Spearman's, and Kendall's correlation coefficients, depending on the data type. As with any use of data, missing data will impact the availability of data, reducing it and potentially affecting the results. Furthermore, the removal of missing-value data from the study when using complete case analysis or available case analysis may result in selection biases. In this paper, we investigate the impact of missing data on the correlation coefficient value by calculating the difference between the correlation coefficient of the original complete dataset and that of a dataset with missing data. Two deletion strategies (Listwise and Pairwise) and three imputation strategies (Mean, k-Nearest Neighbors (k-NN), and Expectation-Maximization) were used to prepare the data before calculating the correlation coefficient. Unique correlation coefficient values were created by converting unique values to a one-dimensional array, and RMSE metrics were used to evaluate the experiments. Eight UCI and Kaggle datasets with different sizes and numbers of attributes were used in this study. The experiment results demonstrate that the Pairwise strategy and k-NN give good results on the correlation coefficient, respectively, when the missing rate is moderate or less. Pairwise uses all the available values and discards only the missing values of the related attribute, while k-NN fills the missing values with new values that produce correlation coefficient values close to the actual values.

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“…However, notwithstanding this strength or robustness, LDA commonly experiences notable challenges, either when the objects (n) size is restricted if compared to the size of the variables (p), or when comparing to a similar number, n and p (Bodnar et al, 2020). As such, a singular issue in the model may be evident (Tharwat et al, 2017), may induce instability in the model itself, may produce a poor quality of the constructed model (Swesi & Bakar, 2019) or even worse, not possible to construct the model (An & Chen, 2009). As a result, accurate classification is doubtful.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Variable Extractions With Lda for Classification of Mixed Variables, and Applications to Medical Data

Hamid

Mahat

Ibrahim

2021

Journal of Information and Communication Technology

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The strategy surrounding the extraction of a number of mixed variables is examined in this paper in building a model for Linear Discriminant Analysis (LDA). Two methods for extracting crucial variables from a dataset with categorical and continuous variables were employed, namely, multiple correspondence analysis (MCA) and principal component analysis (PCA). However, in this case, direct use of either MCA or PCA on mixed variables is impossible due to restrictions on the structure of data that each method could handle. Therefore, this paper executes some adjustments including a strategy for managing mixed variables so that those mixed variables are equivalent in values. With this, both MCA and PCA can be performed on mixed variables simultaneously. The variables following this strategy of extraction were then utilised in the construction of the LDA model before applying them to classify objects going forward. The suggested models, using three real sets of medical data were then tested, where the results indicated that using a combination of the two methods of MCA and PCA for extraction and LDA could reduce the model’s size, having a positive effect on classifying and better performance of the model since it leads towards minimising the leave-one-out error rate. Accordingly, the models proposed in this paper, including the strategy that was adapted was successful in presenting good results over the full LDA model. Regarding the indicators that were used to extract and to retain the variables in the model, cumulative variance explained (CVE), eigenvalue, and a non-significant shift in the CVE (constant change), could be considered a useful reference or guideline for practitioners experiencing similar issues in future.

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Feature Clustering for Pso-Based Feature Construction on High-Dimensional Data

Cited by 6 publications

References 36 publications

A Novel Approach for Data Feature Weighting Using Correlation Coefficients and Min–Max Normalization

A Novel Approach for Data Feature Weighting Using Correlation Coefficients and Min–Max Normalization

Impact of Missing Data on Correlation Coefficient Values: Deletion and Imputation Methods for Data Preparation

Adaptive Variable Extractions With Lda for Classification of Mixed Variables, and Applications to Medical Data

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