An ensemble feature selection algorithm based on PageRank centrality and fuzzy logic

Joodaki, Mehdi; Dowlatshahi, Mohammad Bagher; Joodaki, Nazanin Zahra

doi:10.1016/j.knosys.2021.107538

Cited by 30 publications

(13 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…33 It also enhance the data preprocessing analysis which further increase the machine learning process as irrelevant data generally affect the accuracy and computational cost. 34 Here in this research article three methods has been analyzed for the feature selection procedure, which are as follows:…”

Section: Forecasting Methodologymentioning

confidence: 99%

“…Feature selection: The practice of detecting and deleting as many unnecessary and redundant characteristics as feasible is known as feature subset selection, which decreases the dimensionality of the data, allowing learning algorithms to work more quickly and efficiently 33 . It also enhance the data preprocessing analysis which further increase the machine learning process as irrelevant data generally affect the accuracy and computational cost 34 . Here in this research article three methods has been analyzed for the feature selection procedure, which are as follows: Fisher Test Score: The F ‐test is a type of filter methods which is highly efficient for feature selection and calculates the cross and internal distances with respect to each parameter and selects the parameter having largest distance for both inner and inter class, but overlooks the inter dependency among features in group feature extraction.…”

Section: Forecasting Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Hyper‐parametric improved machine learning models for solar radiation forecasting

Kumar

Namrata

Kumari

2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary Spatiotemporal solar radiation forecasting is extremely challenging due to its dependence on metrological and environmental factors. Chaotic time‐varying and non‐linearity make the forecasting model more complex. To cater this crucial issue, the paper provides a comprehensive investigation of the deep learning framework for the prediction of the two components of solar irradiation, that is, Diffuse Horizontal Irradiance (DHI) and Direct Normal Irradiance (DNI). Through exploratory data analysis the three recent most prominent deep learning (DL) architecture have been developed and compared with the other classical machine learning (ML) models in terms of the statistical performance accuracy. In our study, DL architecture includes convolutional neural network (CNN) and recurrent neural network (RNN) whereas classical ML models include Random Forest (RF), Support Vector Regression (SVR), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGB), and K‐Nearest Neighbor (KNN). Additionally, three optimization techniques Grid Search (GS), Random Search (RS), and Bayesian Optimization (BO) have been incorporated for tuning the hyper parameters of the classical ML models to obtain the best results. Based on the rigorous comparative analysis it was found that the CNN model has outperformed all classical machine learning and DL models having lowest mean squared error and highest R‐Squared value with least computational time.

show abstract

Section: Forecasting Methodologymentioning

confidence: 99%

Section: Forecasting Methodologymentioning

confidence: 99%

Hyper‐parametric improved machine learning models for solar radiation forecasting

Kumar

Namrata

Kumari

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Note that various FS methods may have multiple rankings, and each can be an optimal local subset of features 44 . An ensemble approach is used to acquire the optimal solution by aggregating the local optimum of various FS methods to deal with their weaknesses 55,56 . Ensemble algorithms can be divided into several categories based on different criteria.…”

Section: Principle Concepts and Associated Workmentioning

confidence: 99%

“…44 An ensemble approach is used to acquire the optimal solution by aggregating the local optimum of various FS methods to deal with their weaknesses. 55,56 Ensemble algorithms can be divided into several categories based on different criteria. Mitchell et al 57 presented a group of FS methods using the parallel application of multiple FS algorithms.…”

Section: Ensemble Fsmentioning

confidence: 99%

Ensemble feature selection for multi‐label text classification: An intelligent order statistics approach

Miri

Dowlatshahi

Hashemi

et al. 2022

Int J of Intelligent Sys

Self Cite

View full text Add to dashboard Cite

Because of the overgrowth of data, especially in text format, the value and importance of multi‐label text classification have increased. Aside from this, preprocessing and particularly intelligent feature selection (FS) are the most important step in classification. Each FS finds the best features based on its approach, but we try to use a multi‐strategy approach to find more useful features. Evaluating and comparing features’ importance and relevance makes using multiple strategy and methods more suitable than conventional approaches because each feature is measured based on several perspectives. Nevertheless, the ensemble FS merges the final performance results of various methods to take advantage of different methods’ strengths and better classify. In this article, we have proposed an ensemble FS method for multi‐label text data (MLTD) for the first time using the order statistics (EMFS) approach. We have utilized four multi‐label FS (MLFS) algorithms with various particular performances to achieve a good result. In this method, as one of the most important statistics methods, Order Statistics was used to aggregate the ranks of different algorithms, which is robust against noise, redundant and inessential features. In the end, the performance of EMFS, executing six MLTDs, was evaluated according to six performance criteria (ranking‐based and classification‐based). Surprisingly, the proposed method was more accurate than others among all used MLTDs. The proposed method has improved by 1.5% compared to other methods. This value is based on the results obtained based on six evaluation criteria and all tested data sets.

show abstract

“…The authors of [67] used pageRank algorithm and fuzzy logic to ensemble feature selection techniques. Multi label feature selection was carried out in [68] through the MCDM technique; VIKOR.…”

Section: Related Workmentioning

confidence: 99%

Multi-Criteria Decision Making Methods for Ensemble Feature Selection using q-Rung Orthopair Hesitant Fuzzy Distance and Similarity Measures

Kesavan

Seethapathy

Satheeshkumar

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper deals with ensemble feature selection using the q-rung orthopair hesitant fuzzy multi-criteria decision-making (MCDM) methods including VIse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Combinative distance-based assessment (CODAS). The novalty of this paper is to design the three MCDM algorithms based on q-rung orthopair hesitant fuzzy sets with different distance and similarity measures. The well known distance and similarity measures are to be taken such as Hausdorff measure, hybrid Hausdorff and distance measure, synergetic measure, similarity for Hausdorff measure, similarity for hybrid Hausdorff and distance measure, similarity for synergetic measure, ordered Hausdorff measure, ordered hybrid Hausdorff and distance measure, similarity for ordered Hausdorff measure and similarity for ordered hybrid Hausdorff and distance measure This is the first time in the literature, an ensemble feature selection problem is modeled as a q-rung orthopair hesitant fuzzy MCDM extended to VIKOR, TOPSIS and CODAS techniques with distance and similarity measures. By using q-ROHFS VIKOR, TOPSIS and CODAS methods, a score is assigned to each feature based on the values of the preference matrix. At last, an output rank vector is produced for all features, from which the user can select the desired number of features. To prove the efficiency and optimality of our proposed method, we compared with the basic filter-based feature selections and ensemble feature selection by using feature ranking strategy. Our method is superior and efficient than the ensemble methods based on the accuracy and F-score levels.

show abstract

An ensemble feature selection algorithm based on PageRank centrality and fuzzy logic

Cited by 30 publications

References 42 publications

Hyper‐parametric improved machine learning models for solar radiation forecasting

Hyper‐parametric improved machine learning models for solar radiation forecasting

Ensemble feature selection for multi‐label text classification: An intelligent order statistics approach

Multi-Criteria Decision Making Methods for Ensemble Feature Selection using q-Rung Orthopair Hesitant Fuzzy Distance and Similarity Measures

Contact Info

Product

Resources

About