Fuzzy Based Feature Selection Research Articles

A fog based load forecasting strategy for smart grids using big electrical data

Internet of things (IoT) enables the smart electrical grids (SEGs) to support a lot of tasks throughout the generation, transmission, distribution and consumption of energy. Large amounts of data are generated through these smart devices, which can be sent to the cloud for further processing. Hence, appropriate actions can be taken based on the cloud processing. However, sending the complete captured data directly to the cloud would lead to resource wastage. To overcome the cloud challenges, fog computing tier acts as a bridge in the middle between the IoT devices embedded in SEG and the cloud. Thus, a 3-tiers architecture is proposed to replace 2-tiers one. The added fog tier offers a place for collecting, computing, storing smart meter data before transmitting them to the cloud. In this paper, a new electrical load forecasting (ELF) strategy has been proposed based on the pre-mentioned three tiers architecture. The proposed strategy consists of two phases; which are; (i) data pre-processing phase (DP2) and (ii) load prediction phase (LP2). Both phases take place at a cloud data center (CDC) on the collected data, which is received from all fogs connected to the entire cloud. The aim of the data preprocessing phase is to; (i) reject outliers from the collected data, and (ii) feature selection. The main contribution of this paper lied on the feature selection, which elects only the effective features for the next load prediction phase. Features selection is an essential pre-processing operation to reduce the dataset dimensions from all irrelevant features leading to enable the ELF from taking fast and correct decision for other electrical subsystems. A new feature selection methodology called fuzzy based feature selection (FBFS) is presented, which involves two stages; feature ranking stage (FRS) and feature selection stage (FS2). FBFS is a hybrid method based on both features selection methods filter and wrapper providing fast and more accurate significant features. Then, during LP2, the filtered data is used to give a fast and accurate load prediction. Experimental results have shown that the proposed FBFS promotes the load forecasting efficiency in terms of precision, recall, accuracy and F-measure compared with recent features selection methodologies. FBFS provides the highest precision, recall, and accuracy results. On the other hand, it provides the lowest error value.

Fuzzy based Feature Selection Scheme through Transductive SVM Technique for Cancer Pattern Classification and Prediction

Objectives: A reliable and precise classification of tumor types is of great importance and essential for successful Diagnosis and Drug Discovery. Methods: Gene expression profiling has shown a great prospective in the outcome prediction of different types of Cancers, with the innovation of Microarray Technology. Microarray cancer data, which have been organized as samples versus genes fashion, are being exploited for the tissue sample classification. They are also useful for identifying potential gene markers in each subtype of cancer, that helps to analyze a particular cancer type in a successful manner. Findings: In this paper a new method for classification based on Fuzzy Rough-Set Feature Selection Approach through Transductive SVM Technique, which is called as FFS+TSVM is proposed. Moreover recently, for Cancer Pattern Classification, a combined Consistency based Feature Selection Approach through Transductive Support Vector Machine (CBFS+TSVM) has proposed and its prediction accuracy has encouraged than that of existing other Classifiers. However, from the literature survey, it is revealed that the performance of the existing scheme can be improved if Fuzzy Rough Set Approach for Gene Feature Selection with Transductive Support Vector Machine is employed. The present work is implemented with the help of Bio Weka and studied thoroughly in terms of Computational Cost, Dimensionality Reduction, Threshold, Classification Error and Classification Accuracy. Applications/Improvements: The proposed work outperforms the existing Transductive Support Vector Machine (TSVM) in terms of Dimensionality Reduction, Threshold, Classification Error and Classification Accuracy for various Cancer Patterns.

A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data

Feature (gene) selection and classification of microarray data are the two most interesting machine learning challenges. In the present work two existing feature selection/extraction algorithms, namely independent component analysis (ICA) and fuzzy backward feature elimination (FBFE) are used which is a new combination of selection/extraction. The main objective of this paper is to select the independent components of the DNA microarray data using FBFE to improve the performance of support vector machine (SVM) and Naïve Bayes (NB) classifier, while making the computational expenses affordable. To show the validity of the proposed method, it is applied to reduce the number of genes for five DNA microarray datasets namely; colon cancer, acute leukemia, prostate cancer, lung cancer II, and high-grade glioma. Now these datasets are then classified using SVM and NB classifiers. Experimental results on these five microarray datasets demonstrate that gene selected by proposed approach, effectively improve the performance of SVM and NB classifiers in terms of classification accuracy. We compare our proposed method with principal component analysis (PCA) as a standard extraction algorithm and find that the proposed method can obtain better classification accuracy, using SVM and NB classifiers with a smaller number of selected genes than the PCA. The curve between the average error rate and number of genes with each dataset represents the selection of required number of genes for the highest accuracy with our proposed method for both the classifiers. ROC shows best subset of genes for both the classifier of different datasets with propose method.

A new fuzzy-based feature selection and hybrid TLA–ANN modelling for short-term load forecasting

In this paper, a new hybrid method based on teacher learning algorithm (TLA) and artificial neural network (ANN) is proposed to develop an accurate model to investigate short-term load forecasting more precisely. In contrast to the other evolutionary-based training techniques, the proposed method utilises both the ability of ANNs to generate a non-linear mapping among different complex data as well as the powerful ability of TLA for global search and exploration. In addition, in an attempt to choose the most satisfying features from the set of input variables, a novel feature-selection approach based on fuzzy clustering and fuzzy set theory is proposed and utilised sufficiently. In order to improve the overall performance of TLA for optimisation applications, a new modification phase is proposed to increase the ability of the algorithm to explore the entire search space globally. The simulation results show the feasibility and the superiority of the proposed hybrid method over the other well-known methods in the area.