Attribute Selection Method Research Articles

A New Method Based on Belief Rule Base with Balanced Accuracy and Interpretability for Student Achievement Prediction

In the field of education, the accurate prediction of students’ future performance is essential for personalized instruction and efficient allocation of resources. Such predictions not only help education professionals develop targeted educational strategies but also identify students’ learning needs at an early stage so that timely interventions and support can be provided. To gain the trust of educational experts and ensure the practical application value of the prediction results, the prediction methods used must be highly interpretable. However, there are two problems with the current belief rule base (BRB) applied to student performance prediction. First, there is a current lack of effective strategies for enhancing the interpretability of the optimization process. Second, BRB models that overemphasize accuracy tend to exhibit characteristics of black-box models. To overcome these challenges, this paper proposes a new method based on BRB with balanced accuracy and interpretability (BRB-Bai) for student achievement prediction. First, an attribute selection method is proposed to filter out important features associated with student performance. Then, expert knowledge credibility is calculated, and four interpretability strategies are proposed to ensure the interpretability of the model and to achieve a balance between interpretability and accuracy on the basis of expert knowledge credibility. The effectiveness of the proposed model is demonstrated by conducting experiments on the student achievement dataset.

Information entropy-assisted intuitionistic fuzzy rough feature subset selection

Fuzzy-rough set (FRS) has been effectively implemented as a powerful pre-processing tool to cope with the issues such as vagueness, imprecision, noise and uncertainty in high-dimensional data analysis. However, FRS fails to handle the uncertainty due to identification, which is very much common in heterogeneous data. Moreover, it is always challenging to handle different issues such as vagueness, uncertainty, and noise during elimination of irrelevant and redundant features. This paper investigates an intuitionistic fuzzy rough set (IFRS)-aided feature selection method by using information entropy notion to tackle these issues simultaneously. We establish an intuitionistic fuzzy (IF) similarity relation. Then IFRS model is discussed based on this relation. Next, we present a novel IF granular structure. Based on the granular structure, we present lambda-conditional entropy for IF framework. Further, relevant mathematical theorems are proved to justify the theoretical aspect of the models. Moreover, a positive region preserved attribute selection method is proposed. A comprehensive experimental study is discussed to demonstrate the effectiveness and practical validation of the proposed method. Finally, a methodology is developed based on proposed models, which increases accuracy for the minority RNA silencing suppressors against majority class non-suppressors as evident from better sensitivity and G-means metrics.

Integrating cat boost algorithm with triangulating feature importance to predict survival outcome in recurrent cervical cancer

Cervical cancer is one of the most dangerous malignancies in women. Prolonged survival times are made possible by breakthroughs in early recognition and efficient treatment of a disease.The existing methods are lagging on finding the important attributes to predict the survival outcome. The main objective of this study is to find individuals with cervical cancer who are at greater risk of death from recurrence by predicting the survival.A novel approach in a proposed technique is Triangulating feature importance to find the important risk factors through which the treatment may vary to improve the survival outcome.Five algorithms Support vector machine, Naive Bayes, supervised logistic regression, decision tree algorithm, Gradient boosting, and random forest are used to build the concept. Conventional attribute selection methods like information gain (IG), FCBF, and ReliefFare employed. The recommended classifier is evaluated for Precision, Recall, F1, Mathews Correlation Coefficient (MCC), Classification Accuracy (CA), and Area under curve (AUC) using various methods. Gradient boosting algorithm (CAT BOOST) attains the highest accuracy value of 0.99 to predict survival outcome of recurrence cervical cancer patients. The proposed outcome of the research is to identify the important risk factors through which the survival outcome of the patients improved.

Tree-based Machine Learning Ensembles and Feature Importance Approach for the Identification of Intrusions in UNR-IDD Dataset

Detection of intrusions from network data with the use of machine learning techniques has gained great attention in the past decades. One of the key problems in the network security domain is the availability of representative datasets for testing and evaluation purposes. Despite several efforts by researchers to release datasets that can be used for benchmarking attack detection models, some of the released datasets still suffer from one limitation or the other. Thus, some researchers at the University of Nevada released a dataset named UNR-IDD dataset which was argued to be free from some of the limitations of the past datasets. This study proposed Tree-based ensemble approaches for building binary intrusion identification models from the UNR-IDD dataset. Decision Tree algorithms are used as base classifiers in the Extra Trees, Random Forest and AdaBoost-based intrusion detection models. The results of the experimental analyses carried out indicated that the three ensembles performed excellently when feature selection was used compared to when all features were applied. For instance, Extra Trees model achieved an accuracy of 0.97, precision of 0.98, recall of 0.98 and f1-score of 0.98. Similarly, Random Forest model achieved an accuracy of 0.98, precision of 0.98, recall of 0.99 and f1-score of 0.98. Adaboost-based model had an accuracy of 0.96, precision of 0.96, recall of 0.99 and f1-score of 0.98. It was deduced that Random Forest intrusion classification model achieved slight overall best results when compared to the other models built. It is concluded that the three homogeneous ensemble models achieved very promising results while feature importance was used as attribute selection method.

Exploring the influence of driver’s presence on passengers’ willingness level to take autonomous taxis – Evidence from Seattle metropolitan area

This study aims to identify the potential determinants of people's willingness to adopt autonomous vehicle (AV) taxis, with or without a backup driver, using Binary Logistic Regression in combination with the enhanced Random Forest attribute selection method. The results indicate that young men with frequent use of ride-sharing services, walk frequently, and have household incomes of approximately $150,000 − $200,000 are more likely to be interested in using AV taxis regardless of the presence of a backup driver. The study finds that previous travel habits (use of different modes) can greatly influence individuals' interest levels in using AV taxis. The study points AV taxi service providers should address female passengers' concerns to pursue a larger market. This study innovatively uses individuals’ preferences for their residential locations, such as proximity to workplace, walkability, and affordability. Through the inclusion of these variables in the analysis, this study offers a more comprehensive insight into the potential users of AV taxis, thus enabling policymakers and planners to pursue a transportation system that is sustainable, equitable, and responsive to the diverse needs and preferences of different populations. The study has limitations in that it lacks users' perceived benefits and their specific needs pertaining to AVs. Finally, the study emphasizes the importance of examining the causes of inequality and promoting equitable access to AV technology, particularly for vulnerable groups.

Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria.

Urease, a pivotal enzyme in nitrogen metabolism, plays a crucial role in various microorganisms, including the pathogenic Helicobacter pylori. Inhibiting urease activity offers a promising approach to combating infections and associated ailments, such as chronic kidney diseases and gastric cancer. However, identifying potent urease inhibitors remains challenging due to resistance issues that hinder traditional approaches. Recently, machine learning (ML)-based models have demonstrated the ability to predict the bioactivity of molecules rapidly and effectively. In this study, we present ML models designed to predict urease inhibitors by leveraging essential physicochemical properties. The methodological approach involved constructing a dataset of urease inhibitors through an extensive literature search. Subsequently, these inhibitors were characterized based on physicochemical properties calculations. An exploratory data analysis was then conducted to identify and analyze critical features. Ultimately, 252 classification models were trained, utilizing a combination of seven ML algorithms, three attribute selection methods, and six different strategies for categorizing inhibitory activity. The investigation unveiled discernible trends distinguishing urease inhibitors from non-inhibitors. This differentiation enabled the identification of essential features that are crucial for precise classification. Through a comprehensive comparison of ML algorithms, tree-based methods like random forest, decision tree, and XGBoost exhibited superior performance. Additionally, incorporating the "chemical family type" attribute significantly enhanced model accuracy. Strategies involving a gray-zone categorization demonstrated marked improvements in predictive precision. This research underscores the transformative potential of ML in predicting urease inhibitors. The meticulous methodology outlined herein offers actionable insights for developing robust predictive models within biochemical systems.

A comparison of time series lags and non-lags in Spanish electricity price forecasting using data science models

Abstract Electricity is an indicator that shows the progress of a civilization; it is a product that has greatly changed the way we think about the world. Electricity price forecasting became a fundamental task in all countries due to the deregulation of the electricity market in the 1990s. This work examines the effectiveness of using multiple variables for price prediction given the large number of factors that could influence the price of the electricity market. The tests were carried out over four periods using data from Spain and deep learning models. Two different attribute selection methods based on Pearson’s correlation coefficient have been used to improve the efficiency of the training process. The variables used as input to the different prediction models were chosen, considering those most commonly used previously in the literature. This study attempts to test whether using time series lags improves the non-use of lags. The results obtained have shown that lags improve the results compared to a previous work in which no lags were used.

Deterministic attribute selection for isolation forest

Modern data mining techniques have been gained importance in recent years. In particular, anomaly detection algorithms, applied in key sectors of information technology, have been growing in popularity. One of the efficient and fast algorithms is Isolation Forest. The method consists of two separated stages: Forest formation and evaluation of elements. The first stage relies on forming a forest of isolation trees. Each tree is built in the same manner according to drawn samples and random divisions of data attributes. In this study, an innovative deterministic attribute selection method is proposed, maintaining its random value. New ideas based on imbalance, clustering, and a dispersion of values through non-linear transformation of elements are introduced and thoroughly analyzed. These novel anomaly detection approaches are applied to 25 real datasets, as well as our own artificially generated databases. The Area Under the ROC Curve and the Area Under the PR Curve are used as a measure of the outliers classification quality. The results of the numerical experiment have proven high efficiency and competitive evaluation speed of the proposals in comparison to other Isolation Forest-based approaches, as well as several other popular techniques.

Application of Seismic Attribute Analysis Technology Guided by Model Forward Modeling in L29 Area

Taking the SaErTU and Putaohua oil layers in the L29 well area of LHP oilfield in the northern Songliao Basin as the research object, In response to the difficulty of interference between thin interbedded sand and mudstone and strong reflection between strata, which have a significant impact on fine prediction of sand bodies, a stratigraphic model is established based on the geological characteristics of the target layer to eliminate the impact of stratigraphic reflection; Then add a thin layer of sand body to its interior, establish a thin interlayer model, and obtain a geological model that is more in line with the actual situation of the target layer. Using forward simulation, analyze the seismic response characteristics of sand bodies, extract 30 seismic attributes from 4 categories: amplitude statistics, composite seismic trace statistics, sequence and frequency spectrum statistics, and calculate the correlation between cumulative sandstone thickness and seismic attributes, and select a sensitive seismic attribute set;By combining sedimentary and drilling data, the seismic attribute with the highest sensitivity to the target layer is selected. The multi-level dimensionality reduction and gradual improvement of seismic attribute selection methods using “model forward modeling, attribute analysis, relevant optimization, and drilling implementation” can effectively improve the prediction accuracy of thin interbedded sand bodies and greatly reduce the risk of oilfield exploration and development.

Pendekatan Algoritma Klasifikasi Machine Learning untuk Deteksi Penyakit Demensia

Early detection of dementia through the use of machine learning classification algorithms is important for providing appropriate interventions to patients. In this context, this study aims to compare the performance of several machine learning classification algorithms in detecting dementia using the attribute selection method. In the early stages, patient data including medical history, cognitive test results, and other attributes were collected as input, an attribute selection algorithm was used to select the most informative attribute subset in detecting dementia. The subset of attributes used as input for training machine learning classification models, several classification algorithms such as Extra Trees (ET), Linear Discriminant Analysis (LDA), Random Forest (RF) and Ridge. In this study, accuracy is used as the main metric to compare algorithm performance. The evaluation results show that the Random Forest (RF) algorithm produces the best performance with an accuracy of 91.56%. The Extra Trees (ET) algorithm has an almost comparable accuracy of 91.44%, while Ridge and Linear Discriminant Analysis (LDA) have an accuracy of 90.44% respectively. In the context of dementia detection, the performance of the Random Forest algorithm with the attribute selection method proved to be the best with an accuracy of 91.56%. These results indicate that the developed model is capable of recognizing complex patterns and relationships between features that are relevant to dementia status. The use of the attribute selection method also contributes to increasing the accuracy and efficiency of the classification algorithm.

Traffic flow privacy protection with performance guarantee for classification in large networks (minor revision of INS_D_21_805R3)

Privacy-preserving traffic flow classification has attracted a significant amount of research interest because of its increasing importance to both network management and privacy protection. In this paper, we propose novel methods for effectively protecting network flow identifiers and attributes against privacy inference attacks in port-based and payload-based classifications respectively, and analyze their performance guarantee on data utility for flow classification and privacy (security). For protection of flow identifiers, we propose a partial identifier protection approach applying randomization and anonymization respectively on desired bit positions to conceal sensitive information, and show their expected-case performance guarantee. For protection of flow attributes, we propose a perturbation-based scheme that first selects the representative attributes by deploying an entropy-based attribute selection method to filter out redundant and insignificant attributes and reduce the problem space, then partitions the attribute domains into either equal-depth or equal-width intervals and perturbs attribute values in these intervals by swapping them with those in adjacent intervals and intervals with same value distribution respectively. We analyze the performance guarantee of the proposed methods and show the experiment results of classification accuracy obtained by implementing popular machine-learning based benchmark classifiers on our selected attributes against that on raw attributes, and on our perturbed attribute values against that on raw values, respectively. The experiment results show that our proposed methods for attribute selection and perturbation retain a high degree of data utility under the desired privacy guarantee for network traffic classification.

Identification of a minimum number of genes to predict triple-negative breast cancer subgroups from gene expression profiles

BackgroundTriple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them.MethodsLehman’s TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set.ResultsWe identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug–target interactions were found among the upregulated genes in the M, IM and MSL subsets.ConclusionsOur study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets.

An Experimental Study on Evaluating Alzheimer’s Disease Features using Data Mining Techniques

Alzheimer’s disease (AD) predominantly affects the elderly population with symptoms including, but not limited to, cognitive impairment and memory loss. Predicting AD and mild cognitive impairment (MCI) can lengthen the lifespan of patients and help them to access necessary medical resources. One potential approach to achieve an early diagnosis of AD is to use data mining techniques which explore various characteristic traits related to MCI, cognitively normal (CN), and AD subjects to build classifiers that reveal important contributors to the disease. These classifiers are used by physicians during the AD diagnostic process in a clinical evaluation. In this research, we compare between different data mining algorithms through empirical data approach to deal with the AD diagnosis. Experimental evaluation, using attribute selection methods, and classifiers from rule induction and other classification techniques have been conducted on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI-MERGE). The results illustrate the good classification performance of classifiers with rules in predicting AD.

HANDLING THE EFFECT OF ATTRIBUTE SELECTION ON SUPPORT VECTOR MACHINES FOR DETECTING CHRONIC KIDNEY DISEASE

Chronic kidney disease is a gradual loss of kidney function. Determining the important attributes that describe this disease plays a key role in screening and examining the disease by field specialists. The main aim of this study is to comprehensively compare the attribute selection algorithms for predicting this disease. With this aim, several models were built and compared using well-known performance metrics such as accuracy, sensitivity, and specificity in the experiments. Two different attribute selection methods; the stability selection and the minimum redundancy maximum relevance were compared comprehensively on the unbalanced and balanced datasets. In this framework, the stability selection method gave the important attributes. The support vector machines with radial bases function kernel successfully performed the classification using these attributes for this problem.

Rule-based classifier based on accident frequency and three-stage dimensionality reduction for exploring the factors of road accident injuries.

Road accidents are one of the primary causes of death worldwide; hence, they constitute an important research field. Taiwan is a small country with a high-density population. It particularly has a considerable number of locomotives. Furthermore, Taiwan’s traffic accident fatality rate increased by 23.84% in 2019 compared with 2018, primarily because of human factors. Road safety has long been a challenging problem in Taiwanese cities. This study collected public data pertaining to traffic accidents from the Taoyuan city government in Taiwan and generated six datasets based on the various accident frequencies at the same location. To find key attributes, this study proposes a three-stage dimension reduction to filter attributes, which includes removing multicollinear attributes, the integrated attribute selection method, and statistical factor analysis. We applied five rule-based classifiers to classify six different frequency datasets and generate the rules of accident severity. The order of top ten key attributes was hit vehicle > certificate type > vehicle > action type > drive quality > escape > accident type > gender > job > trip purposes in the maximum accident frequency CF ≥ 10 dataset. When locomotives, bicycles, and people collide with other locomotives or trucks, injury or death can easily occur, and the motorcycle riders are at the highest risk. The findings of this study provide a reference for governments and stakeholders to reduce the road accident risk factors.

Fuzzy Logic and Machine Learning-Enabled Recommendation System to Predict Suitable Academic Program for Students

In recent years, educational data mining has gained a considerable lot of interest as a consequence of the large number of pedagogical content that can be gathered from a range of sources. This is because there is a lot of instructional information that can be obtained. The data mining tools collaborate with academics to improve students’ learning strategies by analyzing, sifting through, and estimating components that are pertinent to students’ characteristics or patterns of behavior. This is accomplished through the following steps: EDM is utilized in the vast majority of instances to develop the classification model, which then assigns a certain class to each student based on the known properties of the training dataset. Before putting the classification model into use, it is possible to utilize a test dataset to verify that the model is accurate. This article provides a description of a recommendation system that determines the most beneficial academic program for students by utilizing fuzzy logic and machine learning. The compilation of a student dataset has begun. It includes a total of 21 features and 1000 individual cases. The initial step is to employ the CFS attribute selection method. This methodology selects 15 of the initial set of 21 characteristics. Following the completion of the data gathering, it is put through various machine learning methods such fuzzy SVM, random forest, and C4.5. This methodology that has been offered makes predictions about the academic program that is best suitable for students.

A Decisive Metaheuristic Attribute Selector Enabled Combined Unsupervised-Supervised Model for Chronic Disease Risk Assessment.

Advanced predictive analytics coupled with an effective attribute selection method plays a pivotal role in the precise assessment of chronic disorder risks in patients. Traditional attribute selection approaches suffer from premature convergence, high complexity, and computational cost. On the contrary, heuristic-based optimization to supervised methods minimizes the computational cost by eliminating outlier attributes. In this study, a novel buffer-enabled heuristic, a memory-based metaheuristic attribute selection (MMAS) model, is proposed, which performs a local neighborhood search for optimizing chronic disorders data. It is further filtered with unsupervised K-means clustering to remove outliers. The resultant data are input to the Naive Bayes classifier to determine chronic disease risks' presence. Heart disease, breast cancer, diabetes, and hepatitis are the datasets used in the research. Upon implementation of the model, a mean accuracy of 94.5% using MMAS was recorded and it dropped to 93.5% if clustering was not used. The average precision, recall, and F-score metric computed were 96.05%, 94.07%, and 95.06%, respectively. The model also has a least latency of 0.8 sec. Thus, it is demonstrated that chronic disease diagnosis can be significantly improved by heuristic-based attribute selection coupled with clustering followed by classification. It can be used to develop a decision support system to assist medical experts in the effective analysis of chronic diseases in a cost-effective manner.

Construction and Optimization of Mental Health Education Consultation Management System Based on Decision Tree Association Rule Mining

This paper studies association rule mining and decision tree algorithm, focusing on the extended research of association rule mining, including the number of generated rules, mining association rules of long itemsets with low support, attribute selection criteria and multivalue attributes in decision tree algorithm. This paper conducts in-depth research and analysis on the design and optimization of the mental health education consultation management system using the association rule decision tree algorithm. This paper analyzes the meaning of parameters under the support-confidence-interest model, and uses regression method to design equations between the number of rules and parameters. We use the multiple correlation coefficient to test the fitting effect of the equation, and use the significance test to verify whether the coefficient of the parameter is significantly zero. On the one hand, the widely used psychological crisis prevention measures generally include the screening of the SCL psychological scale in the early stage of first-year enrolment, the holding of general psychological knowledge lectures and courses, and the opening of psychological counselling rooms with a low penetration rate, but these practices are to a certain extent. In other words, it cannot enable the student administrator to grasp the psychological status of the students in a timely, effective, and dynamic manner, to timely intervene in the possible crisis. Not only the number of attribute values of the current node is considered but also the size of the variable precision clear area of the lower node is considered, that is, the two-layer nodes of the tree are considered at the same time. The new attribute selection method not only overcomes the shortcomings of the original algorithm, but also has the advantages of variable precision rough sets. This paper uses a new criterion for attribute selection, weighted roughness and complexity, which comprehensively considers the classification accuracy and the number of branches. In order to reduce the influence of noisy data and missing values, the algorithm uses a class prediction method based on matching degree. Through comparative experiments, the effectiveness of the method proposed in this paper is verified. We propose a new calculation formula for the similarity of the child nodes of the label set to evaluate the effect of attribute classification, and comprehensively consider the situation that the elements in the two multilabel sets appear or not appear at the same time, so that the calculation of the similarity of the label set is more comprehensive. The experimental results show that the model proposed in this paper can excavate the dialectical combination of multiple factors. By comparing with the existing algorithms, the classification effect of the proposed algorithm is verified. The classification algorithm proposed in this paper is more suitable for dealing with multi-value attributes and multiclass data classification problems. The psychological evaluation and counselling system designed in this paper has achieved the expected goal. The results of this paper can improve the problems existing in the work of psychological counselling services.

Using Satellite Imagery to Improve Local Pollution Models for High-Voltage Transmission Lines and Insulators

This paper addresses the regression modeling of local environmental pollution levels for electric power industry needs, which is fundamental for the proper design and maintenance of high-voltage transmission lines and insulators in order to prevent various hazards, such as accidental flashovers due to pollution and the resultant power outages. The primary goal of our study was to increase the precision of regression models for this application area by exploiting additional input attributes extracted from satellite imagery and adjusting the modeling methodology. Given that thousands of different attributes can be extracted from satellite images, of which only a few are likely to contain useful information, we also explored suitable feature selection procedures. We show that a suitable combination of attribute selection methods (relief, FSRF-Test, and forward selection), regression models (random forest models and M5P regression trees), and modeling methodology (estimating field-measured values of target variables rather than their upper bounds) can significantly increase the total modeling accuracy, measured by the correlation between the estimated and the true values of target variables. Specifically, the accuracies of our regression models dramatically rose from 0.12–0.23 to 0.40–0.64, while their relative absolute errors were conversely reduced (e.g., from 1.04 to 0.764 for the best model).

Hybrid filter–wrapper attribute selection with alpha-level fuzzy rough sets

Selection of important attributes/features from decision information systems plays a vital role in data mining and machine learning tasks. It is regarded as a very interesting, but challenge problem, especially when faced with continuous numerical/real attributes. Neighborhood rough sets and fuzzy rough sets based attribute selection methods are well-known for dealing effectively with numerical/real attributes. However, characteristics of data may be described incompletely by neighborhood classes in the neighborhood rough set model, while the fuzzy rough sets based approach is still quite time-consuming because of the complex calculations on fuzzy equivalence classes. To address these limitations, we apply the concept of sets of level α (α-cut sets) in the fuzzy set theory to construct α-level fuzzy equivalence classes which provide a foundation for developing basic concepts of a new α-level fuzzy rough set model. We will see that under the properties of the α-cut sets, the α-level fuzzy equivalence classes not only help to significantly reduce the computational cost, but also preserve most of the information about the relationships between the objects, and even can decrease some noise in the data. Based on the α-level fuzzy rough set model, we define new reducts and then propose the FSFCF algorithm for attribute subset selection from the decision information systems containing continuous data. It is important to emphasize some advantages of the proposed method. First, in order to evaluate and select optimal attributes, we use an α-level fuzzy certainty factor with the comprehensive consideration to all objects in the universe. Second, the FSFCF algorithm is designed in the hybrid filter–wrapper approach to reduce the size of selected attribute subset as well as enhance the classification accuracy. Therefore, the proposed method can significantly improve the performance of the attribute selection for continuous data. To verify the effectiveness of FSFCF, we implement experiments on a variety of real-world data sets. The results demonstrated that the proposed method outperforms the compared state-of-the-art methods in terms of the computational time, the size of reduct and the classification accuracy for almost all of data sets.