A Fuzzy System for Combining Filter Features Selection Methods

Cateni, Silvia; Colla, Valentina; Vannucci, Marco

doi:10.1007/s40815-016-0208-7

Cited by 32 publications

(15 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to evaluate the proposed method, its accuracy has been compared with other features selection methods in Table 5 . Our dataset has been used to implement on proposed method in [ 23 ], where the parameters of fuzzy membership functions were constant. Table 5 demonstrates that our method outperforms the suggested method in [ 23 ].…”

Section: Resultsmentioning

confidence: 99%

“…Our dataset has been used to implement on proposed method in [ 23 ], where the parameters of fuzzy membership functions were constant. Table 5 demonstrates that our method outperforms the suggested method in [ 23 ]. Receiver Operating Characteristic (ROC) has been also applied to compare the results of the proposed method with other feature selection algorithms ( Figure 6 ).…”

Section: Resultsmentioning

confidence: 99%

“…Fuzzy logic can be useful for solving the multi-objective optimization problem of feature selection process to maximize the model accuracy and minimize the number of used features and avoid weighting of different goals in this kind of optimization problems [ 22 ]. Cateni et al in [ 23 ] designed a method for feature selection by combining four different filter methods using fuzzy logic to specify the importance degree of each feature. Features that have an importance degree more than a predefined threshold value could be exploited as inputs to classifiers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection

Arefnezhad

Samiee

Eichberger

et al. 2019

Sensors

View full text Add to dashboard Cite

This paper presents a novel feature selection method to design a non-invasive driver drowsiness detection system based on steering wheel data. The proposed feature selector can select the most related features to the drowsiness level to improve the classification accuracy. This method is based on the combination of the filter and wrapper feature selection algorithms using adaptive neuro-fuzzy inference system (ANFIS). In this method firstly, four different filter indexes are applied on extracted features from steering wheel data. After that, output values of each filter index are imported as inputs to a fuzzy inference system to determine the importance degree of each feature and select the most important features. Then, the selected features are imported to a support vector machine (SVM) for binary classification to classify the driving conditions in two classes of drowsy and awake. Finally, the classifier accuracy is exploited to adjust parameters of an adaptive fuzzy system using a particle swarm optimization (PSO) algorithm. The experimental data were collected from about 20.5 h of driving in the simulator. The results show that the drowsiness detection system is working with a high accuracy and also confirm that this method is more accurate than the recent available algorithms.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection

Arefnezhad

Samiee

Eichberger

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Many methods have been proposed to handle information of both quantitative and qualitative information with uncertainty, such as fuzzy set theory [16][17][18][19][20], rough set [21,22], uncertainty theory [23,24], evidential reason approach [25][26][27]. These methods have been widely used in many areas, such as supplier selection [28,29], data fusion [30], risk assessments [31,32], business decision-making [33,34] and so on [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

A Modified D Numbers’ Integration for Multiple Attributes Decision Making

Wang

Liu

Wei

2017

Int. J. Fuzzy Syst.

View full text Add to dashboard Cite

For multiple attributes decision making (MADM), D numbers theory has been widely used to deal with uncertain and incomplete information. However, the incomplete information is abandoned in the D numbers' integration representation. This results in unreasonable conclusions in some real world applications. To overcome this drawback, this paper proposes an improved D numbers' integration representation method, by effectively allocating the incomplete information into decision making according to the original value of D numbers. The proposed method is applied to assess the performance of different types of motorcycles. The results show that the proposed method can effectively increase both the accuracy and efficiency of assessment when compared with the original D numbers theory.

show abstract

“…shows a general scheme of the proposed method. The fuzzy approach to outlier detection[49][50] combines some traditional methods through a Fuzzy Inference System (FIS). Four features are computed through the conventional techniques listed below: -Distribution based method.…”

mentioning

confidence: 99%

A Procedure for Building Reduced Reliable Training Datasets from Real-World Data

Cateni

Colla

Vannucci

et al. 2014

Software Engineering / 811: Parallel and Distributed Computing and Networks / 816: Artificial Intelligence and Applications

Self Cite

View full text Add to dashboard Cite

Dimensionality reduction and anomalous data detection are important tasks in machine learning and data mining applications. Many real-world datasets are affected by errors and variable redundancy and this fact can generate problems when the data are used to develop accurate models exploiting some training procedures for parameters tuning. In this paper an automatic procedure is proposed combining detection of unreliable data and reduction of dimensionality to be adopted before exploiting the data to develop a model for prediction purposes. The method has been tested on several datasets belonging to the UCI repository and industrial fields. The results of tests are showed and discussed in the paper. The proposed approach provide a good prediction accuracy providing a minimal but essential dataset.

show abstract

A Fuzzy System for Combining Filter Features Selection Methods

Cited by 32 publications

References 35 publications

Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection

Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection

A Modified D Numbers’ Integration for Multiple Attributes Decision Making

A Procedure for Building Reduced Reliable Training Datasets from Real-World Data

Contact Info

Product

Resources

About