Rough Set Based Feature Selection Research Articles

Noise-resistant fuzzy multineighbourhood rough set-based feature selection with label enhancement and its application for multilabel classification

Noise pollution in process of feature selection greatly reduces the classification efficacy of multilabel data, and the estimation of label descriptions is insufficient because of the lack of label enhancement for label distribution learning. To overcome the limitations, this work presents a noise-resistant fuzzy multineighbourhood rough set-based feature selection methodology with label enhancement and its application for multilabel classification. First, under the fuzzy T-equivalence relation, to construct new adaptive fuzzy neighbourhood class, the multineighbourhood radius set on feature space is integrated with fuzzy neighbourhood radius via sample interval on label space; then, the adaptive fuzzy multineighbourhood granules are obtained. Second, by integrating the parameterized fuzzy decision of labels and adaptive fuzzy multineighbourhood granules, noise-resistant lower and upper approximations via fuzzy multineighbourhood are constructed. A noise-resistant multilabel fuzzy multineighbourhood rough set model and its noise-resistant approximate accuracy can be proposed. Third, the description degree of label is computed to design label proportion after label enhancement. Certain fuzzy multineighbourhood entropy measures are developed to obtain label enhancement-based mutual information. When the algebraic and information perspectives are fused, label enhancement-based mutual information with fuzzy multineighbourhood via approximate accuracy is presented to evaluate the final association relationship between the features and label sets. Finally, a multilabel feature selection strategy via label enhancement will be constructed to obtain the best feature subset. The experimental results applied to 14 multilabel datasets indicate that this algorithm is significant.

Modeling methods for deep fuzzy inference systems based on feature selection

A bottom-up, layer-by-layer feature selection modeling method is proposed for deep fuzzy inference systems. By selecting features in the input space, redundant and interfering features in each layer's input space can be removed. The selected features have been appropriately ordered to avoid the randomness of features within the feature sliding window. Based on fuzzy rough set theory, the matching degree between the fuzzy granules associated with the conditional feature set and those associated with the decision feature is used as a likelihood function. The λ-AIC criterion with a penalty term have been redefined as an evaluation index for features. The obtained model is compared to four baseline models and an existing deep fuzzy rule classification system. The results show that the feature selection-based deep fuzzy inference system effectively improves data classification performance and efficiency. Compared to four rough set-based feature selection methods based on different measures, the method proposed is more convincing in terms of accuracy and the number of features selected. Compared to the modeling method where input space features are randomly shuffled, the feature sorting modeling method performs better and avoids the instability in model accuracy caused by randomness.

COVID-19 drug repurposing model based on pigeon-inspired optimizer and rough sets theory

Discovering the most effective anti-SARS-CoV-2 drugs is the optimal solution to get back to a normal life without COVID-19. Drug repurposing, also known as drug repositioning, has become one of the most important solutions for developing new COVID-19 drugs. However, this alternative requires long-term laboratory experiments to reach the optimal drug that involves the best combination of drug features to resist the COVID-19 virus. In response to this challenge, the COVID-19 drug repurposing (C19-DR) model based on pigeon-inspired optimizer (PIO) and rough sets theory (RST) is proposed. The proposed model presents a new rough set-based feature selection technique that uses a pigeon-inspired optimizer algorithm to find and validate the optimal reduct of drug features to design an effective COVID-19 drug. Moreover, the proposed model can investigate the efficiency of multiple medications against the COVID-19 virus based on the half-maximal inhibitory concentration (IC50) threshold. The effectiveness of the proposed COVID-19 drug repurposing model has been validated using a laboratory drug dataset consisting of 60 medications. The practical results show that the optimized rough set reduct of {hydrogen bonding acceptor (HBA) and number of chiral centers} is the most significant reduct that can be used to design an effective COVID-19 drug. Moreover, the proposed drug design model could verify the efficiency of a selected dataset of drug models based on evaluating the IC50 metric. The verification results proved the high effectiveness of the proposed model in evaluating the predicted IC50 with an accuracy of 91.4% and MSE of 0.034. These findings might be a promising solution that can assist researchers in developing and repurposing novel medications to treat COVID-19 and its new viral mutants.

Exploring the synergistic effects of hybrid grid partitioning and rough set method for fuzzy rule generation in dataset classification

This research explores the synergistic effects of hybrid grid partitioning and the rough set method for fuzzy rule generation in dataset classification. The aim is to improve the accuracy and interpretability of the classification process. The rough set-based feature selection technique is employed to identify the most relevant features for classification, leading to a focused and informative feature subset. The hybrid grid partitioning approach combines clustering algorithms and grid-based methods to create an efficient grid structure, capturing the intrinsic data distribution. This enhances the representation and separation of data regions, improving classification accuracy. The generated fuzzy rule base provides interpretable decision rules, enabling domain experts to gain insights into the classification process. The proposed approach strikes a balance between accuracy and interpretability, making it valuable for various domains. However, limitations such as generalizability and scalability should be considered. Comparative analysis with existing methods and real-world case studies would further validate the effectiveness of the approach. Overall, this research contributes to the advancement of dataset classification and provides a novel integrated approach for accurate and interpretable classification.

RoughSet based Feature Selection for Prediction of Breast Cancer

RoughSet based Feature Selection for Prediction of Breast Cancer

TFSFB: Two-stage feature selection via fusing fuzzy multi-neighborhood rough set with binary whale optimization for imbalanced data

Obtaining informative features is crucial in imbalanced classification. However, existing neighborhood rough set-based feature selection approaches easily overlook the diversity and complexity of data distributions, and it is difficult to obtain this global optimal feature subset from imbalanced and high-dimensional datasets. To tackle these limitations, we construct a new two-stage feature subset selection scheme by fusing the fuzzy multi-neighborhood rough set (FMRS) with the binary whale optimization algorithm (BWOA) for imbalanced data. First, to evaluate those distributions of different features, this standard deviation coefficient is introduced to construct a fuzzy multi-neighborhood radius set. Then, the fuzzy multi-neighborhood granule and fuzzy membership degree are presented to establish the novel FMRS, and the feature significance measure in the view of algebra is developed to balance the approximate properties and influences of different features in the negative and positive classes. Second, fuzzy multi-neighborhood conditional entropy is defined to maximize information quantity of class- imbalanced data from the information view, and then by fusing the two evaluation perspectives above, this mixed metric is provided to fully assess this uncertainty of class-imbalanced datasets. These internal and external significant metrics are designed to obtain this preselected candidate set of features based on the filter FMRS model at this first stage. Third, a control factor can be developed to dominate the whale position update, and a novel fitness function will be constructed when fusing the dependency degree and entropy measure with the reduction ratio to evaluate this optimal subset of features. Adopting population partitioning and local interference schemes can prevent the BWOA from becoming trapped within a local optimum. To reduce this search space of evolution, the dynamic bitmask is used to improve the BWOA, and then an optimal subset of features is acquired through continuous iterations of this wrapper BWOA at this second stage. Finally, a new two-stage algorithm for feature subset selection by fusing FMRS and BWOA is provided to process class-imbalanced data, where this particle swarm optimization algorithm confirms those optimized parameters. Experiments on 31 datasets show that our algorithm is efficient and can achieve excellent classification efficiency for binary and multiclass imbalanced data.

Feature selection based on neighborhood rough sets and Gini index.

Neighborhood rough set is considered an essential approach for dealing with incomplete data and inexact knowledge representation, and it has been widely applied in feature selection. The Gini index is an indicator used to evaluate the impurity of a dataset and is also commonly employed to measure the importance of features in feature selection. This article proposes a novel feature selection methodology based on these two concepts. In this methodology, we present the neighborhood Gini index and the neighborhood class Gini index and then extensively discuss their properties and relationships with attributes. Subsequently, two forward greedy feature selection algorithms are developed using these two metrics as a foundation. Finally, to comprehensively evaluate the performance of the algorithm proposed in this article, comparative experiments were conducted on 16 UCI datasets from various domains, including industry, food, medicine, and pharmacology, against four classical neighborhood rough set-based feature selection algorithms. The experimental results indicate that the proposed algorithm improves the average classification accuracy on the 16 datasets by over 6%, with improvements exceeding 10% in five. Furthermore, statistical tests reveal no significant differences between the proposed algorithm and the four classical neighborhood rough set-based feature selection algorithms. However, the proposed algorithm demonstrates high stability, eliminating most redundant or irrelevant features effectively while enhancing classification accuracy. In summary, the algorithm proposed in this article outperforms classical neighborhood rough set-based feature selection algorithms.

A Novel Neighborhood Rough Set-Based Feature Selection Method and Its Application to Biomarker Identification of Schizophrenia.

Feature selection can disclose biomarkers of mental disorders that have unclear biological mechanisms. Although neighborhood rough set (NRS) has been applied to discover important sparse features, it has hardly ever been utilized in neuroimaging-based biomarker identification, probably due to the inadequate feature evaluation metric and incomplete information provided under a single-granularity. Here, we propose a new NRS-based feature selection method and successfully identify brain functional connectivity biomarkers of schizophrenia (SZ) using functional magnetic resonance imaging (fMRI) data. Specifically, we develop a new weighted metric based on NRS combined with information entropy to evaluate the capacity of features in distinguishing different groups. Inspired by multi-granularity information maximization theory, we further take advantage of the complementary information from different neighborhood sizes via a multi-granularity fusion to obtain the most discriminative and stable features. For validation, we compare our method with six popular feature selection methods using three public omics datasets as well as resting-state fMRI data of 393 SZ patients and 429 healthy controls. Results show that our method obtained higher classification accuracies on both omics data (100.0%, 88.6%, and 72.2% for three omics datasets, respectively) and fMRI data (93.9% for main dataset, and 76.3% and 83.8% for two independent datasets, respectively). Moreover, our findings reveal biologically meaningful substrates of SZ, notably involving the connectivity between the thalamus and superior temporal gyrus as well as between the postcentral gyrus and calcarine gyrus. Taken together, we propose a new NRS-based feature selection method that shows the potential of exploring effective and sparse neuroimaging-based biomarkers of mental disorders.

Rough Set-Based Feature Selection for Predicting Diabetes Using Logistic Regression with Stochastic Gradient Decent Algorithm

Disease prediction and decision-making plays an important role in medical diagnosis. Research has shown that cost of disease prediction and diagnosis can be reduced by applying interdisciplinary approaches. Machine learning and data mining techniques in computer science are proven to have high potentials by interdisciplinary researchers in the field of disease prediction and diagnosis. In this research, a new approach is proposed to predict diabetes in patients. The approach utilizes stochastic gradient descent which is a machine learning technique to perform logistic regression on a dataset. The dataset is populated with eight original variables (features) collected from patients before being diagnosed with diabetes. The features are used as input values in the proposed approach to predict diabetes in the patients. To examine the effect of having the right variable in the process of making predictions, five variables are selected from the dataset based on rough set theory (RST). The proposed approach is applied again but this time on the selected features to predict diabetes in the patients. The results obtained from both applications have been documented and compared as part of the approach evaluations. The results show that the proposed approach improves the accuracy of predicting diabetes when RST is used to select variables for making the prediction. This paper contributes toward the ongoing efforts to find innovative ways to improve the prediction of diabetes in patients.

Design of metaheuristic rough set-based feature selection and rule-based medical data classification model on MapReduce framework

Abstract Recently, big data analytics have gained significant attention in healthcare industry due to generation of massive quantities of data in various forms such as electronic health records, sensors, medical imaging, and pharmaceutical details. However, the data gathered from various sources are intrinsically uncertain owing to noise, incompleteness, and inconsistency. The analysis of such huge data necessitates advanced analytical techniques using machine learning and computational intelligence for effective decision making. To handle data uncertainty in healthcare sector, this article presents a novel metaheuristic rough set-based feature selection with rule-based medical data classification (MRSFS-RMDC) technique on MapReduce framework. The proposed MRSFS-RMDC technique designs a butterfly optimization algorithm for minimal rough set selection. In addition, Hadoop MapReduce is applied to process massive quantity of data. Moreover, a rule-based classification approach named Repeated Incremental Pruning for Error Reduction (RIPPER) is used with the inclusion of a set of conditional rules. The RIPPER will scale in a linear way with the number of training records utilized and is suitable to build models with data uncertainty. The proposed MRSFS-RMDC technique is validated using benchmark dataset and the results are inspected under varying aspects. The experimental results highlighted the supremacy of the MRSFS-RMDC technique over the recent state of art methods in terms of different performance measures. The proposed methodology has achieved a higher F-score of 96.49%.

Local rough set-based feature selection for label distribution learning with incomplete labels

Local rough set-based feature selection for label distribution learning with incomplete labels

An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network

In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy rough set-based feature selection (FRSFS) technique is associated with a Random weight neural network (RWNN) classifier to obtain the better generalization ability. Results on different dataset show that the proposed technique performs well and provides better speed and accuracy when compared by associating FRSFS with other machine learning classifiers (i.e., KNN, Naive Bayes, SVM, decision tree and backpropagation neural network).

Rough set-based feature selection for weakly labeled data

Supervised learning is an important branch of machine learning (ML), which requires a complete annotation (labeling) of the involved training data. This assumption is relaxed in the settings of weakly supervised learning, where labels are allowed to be imprecise or partial. In this article, we study the setting of superset learning, in which instances are assumed to be labeled with a set of possible annotations containing the correct one. We tackle the problem of learning from such data in the context of rough set theory (RST). More specifically, we consider the problem of RST-based feature reduction as a suitable means for data disambiguation, i.e., for the purpose of figuring out the most plausible precise instantiation of the imprecise training data. To this end, we define appropriate generalizations of decision tables and reducts, using tools from generalized information theory and belief function theory. Moreover, we analyze the computational complexity and theoretical properties of the associated computational problems. Finally, we present results of a series of experiments, in which we analyze the proposed concepts empirically and compare our methods with a state-of-the-art dimensionality reduction algorithm, reporting a statistically significant improvement in predictive accuracy.

Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review

AbstractFeature selection aims to select a feature subset from an original feature set based on a certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it has become a key method for data preprocessing in many data mining tasks. Recently, many feature selection strategies have been developed since in most cases it is infeasible to obtain an optimal/reduced feature subset by using exhaustive search. Among these strategies, fuzzy rough set theory has proved to be an ideal candidate for dealing with uncertain information. This article provides a comprehensive review on the fuzzy rough set theory and two fuzzy rough set theory based feature selection methods, that is, fuzzy rough set based feature selection methods and fuzzy rough neural network based feature selection methods. We review the publications related to the fuzzy rough theory and its applications in feature selection. In addition, the challenges in the two types of feature selection methods are also discussed.This article is categorized under: Technologies > Machine Learning

Rough sets and social ski-driver optimization for drug toxicity analysis

Background and objectives: Toxicity testing is an important step for developing new drugs, and animals are widely used in this step by exposing them to the toxicants. Zebrafishes are widely used for measuring and detecting the toxicity. However, measuring and testing toxicity manually is not feasible due to the large number of embryos. This work presents an automated model to investigate the toxicity of two toxicants (3, 4-Dichloroaniline (34DCA) and p-Tert-Butylphenol (PTBP)).Methods: The proposed model consists of two steps. In the first step, a set of features is extracted from microscopic images of zebrafish embryos using the Segmentation-Based Fractal Texture Analysis (SFTA) technique. Secondly, a novel rough set-based model using Social ski-driver (SSD) is used to find a global minimal subset of features that preserves important information of the original features. In the third step, the AdaBoost classifier is used to classify an unknown sample to alive or coagulant after exposing the embryo to a toxic compound.Results: For detecting the toxicity, the proposed model is compared with (i) three deterministic rough set reduction algorithms and (ii) the PSO-based algorithm. The classification performance rate of our model was ranged from 97.1% to 99.5% and it outperformed the other algorithms.Conclusions: The results of our experiments proved that the proposed drug toxicity model is efficient for rough set-based feature selection and it obtains a high classification performance.

Machine learning approach for wart treatment selection: prominence on performance assessment

Warts are benign tumors, caused by human papillomavirus (HPV). The present study mainly emphasis on the selection of suitable methods for the removal of a common and plantar wart. There are numerous wart treatment methods for this disease, among them cryotherapy and immunotherapy are well-known approaches. Identifying the suitable wart treatment method manually is quite challenging. Moreover, existing machine learning (ML) techniques show a poor prediction accuracy towards the selection of wart treatment method, however, the prediction accuracy is not satisfactory and can be further improved. To achieve the same, the current study utilizes the advantage of fuzzy rough set based feature selection (FRFS) to generate the most optimal informative feature space, which in turn makes the ML algorithms more accurate and leads to a better prognosis. The proposed FRFS based Naive Bayes and FRFS based CART models outperform from the existing model in terms of prediction accuracy.

A Distributed Rough Evidential K-NN Classifier: Integrating Feature Reduction and Classification

The Evidential K-Nearest Neighbor (EK-NN) classification rule provides a global treatment of imperfect knowledge in class labels, but still suffers from the curse of dimensionality as well as runtime and memory restrictions when performing nearest neighbors search, in particular for large and high-dimensional data. To avoid the curse of dimensionality, this article first proposes a rough evidential K-NN (REK-NN) classification rule in the framework of rough set theory. Based on a reformulated K-NN rough set model, REK-NN selects features and thus reduces complexity by minimizing a proposed neighborhood pignistic decision error rate, which considers both Bayes decision error and spatial information among samples in feature space. In contrast to existing rough set-based feature selection methods, REK-NN is a synchronized rule rather than a stepwise one, in the sense that feature selection and learning are performed simultaneously. In order to further handle data with large sample size, we derive a distributed REK-NN method and implement it in the Apache Spark. The theoretical analysis of the classifier generalization error bound is finally presented. It is shown that the distributed REK-NN achieves good performances while drastically reducing the number of features and consuming less runtime and memory. Numerical experiments conducted on real-world datasets validate our conclusions.

Range Specific Neighborhood Rough Set Based Feature Selection for Driver Inattention Classification

In the view of performance improvement in machine learning algorithms it is essential to feed them with relevant features. Feature selection is one of the evident process followed by most of the learning algorithms for choosing the relevant features towards reducing the dimensionality of the dataset as well as to improve the classification accuracy. Among various feature selection techniques, Rough Set Theory (RST) has its own major contributions to feature selection domain. However, the conventional rough set based feature selection procedure makes binary decision on either marking an attribute as relevant or irrelevant.The fuzzy based Rough Set could resolve this problem by finding the relevancy by using membership values, however, this method is unable to identify the boundary or range of an attribute value which is appropriate for classification. The idea of feature selection is inappropriate when specific range of an attribute value represents a decision variable while seems to be irrelevant when it’s complete range is considered. This research work focuses on choosing relevant features for the problem of driver inattention detection. The features extracted for the focused problem are real numbers, hence the Neighborhood Rough Set (NRS) model is followed here rather than conventional Rough Set(RS) approach. In this paper, a Range specific Neighborhood Rough Set (RNRS) based feature selection is proposed for more accurate feature selection for the application of detecting driver’s inattention problem. The experiments are carried out with three real time driver datasets and the results are reported to prove the significance of the proposed RNRS based feature selection. Two learning algorithms, namely K-nearest neighbors and support vector machines are usedto evaluate the performance of the proposed approach. Theresults show that the proposed algorithm can significantlyimprove the classification performance.

Fuzzy Rough Set Based Feature Selection for Large-Scale Hierarchical Classification

The classification of high-dimensional tasks remains a significant challenge for machine learning algorithms. Feature selection is considered to be an indispensable preprocessing step in high-dimensional data classification. In the era of big data, there may be hundreds of class labels, and the hierarchical structure of the classes is often available. This structure is helpful in feature selection and classifier training. However, most current techniques do not consider the hierarchical structure. In this paper, we design a feature selection strategy for hierarchical classification based on fuzzy rough sets. First, a fuzzy rough set model for hierarchical structures is developed to compute the lower and upper approximations of classes organized with a class hierarchy. This model is distinguished from existing techniques by the hierarchical class structure. A hierarchical feature selection problem is then defined based on the model. The new model is more practical than existing feature selection approaches, as many real-world tasks are naturally cast in terms of hierarchical classification. A feature selection algorithm based on sibling nodes is proposed, and this is shown to be more efficient and more versatile than flat feature selection. Compared with the flat feature selection algorithm, the computational load of the proposed algorithm is reduced from 98.0% to 6.5%, while the classification performance is improved on the SAIAPR dataset. The related experiments also demonstrate the effectiveness of the hierarchical algorithm.

Rough set-based feature selection for credit risk prediction using weight-adjusted boosting ensemble method

With the tremendous development of financial institutions, credit risk prediction (CRP) plays an essential role in granting loans to customers and helps them to minimize their loss because credit approval sometimes results in massive financial loss. So extra attention is needed to identify risky customer. Researchers have designed complex CRP models using artificial intelligence (AI) and statistical techniques to support the financial institutions to take correct business decisions. Though there are various statistical and AI methods available, the recent literature shows that the ensemble-based CRP model provides improved prediction results than single classifier system. The small increase in the performance of CRP model could result in a significant improvement in the profit of financial institutions and banks. This work proposes a weight-adjusted boosting ensemble method (WABEM) using rough set (RS)-based feature selection (FS) technique with the balancing and regression-based preprocessing called RS$$\_$$RFS-WABEM. Regression is used to fill missing value in the records to improve the performance of CRP. Three credit datasets (Australia, German and Japanese) are chosen to validate the feasibility and effectiveness of the proposed ensemble method. The trade-off between the uncertainty and imprecise probability of the proposed classifier model is evaluated using the performance measures such as accuracy and area under the curve. Experimental results show that the proposed ensemble method performs better than other base and ensemble classifier methods.