Elimination Of Redundant Features Research Articles

A novel multivariate combined power load forecasting system based on feature selection and multi-objective intelligent optimization

The ongoing transition in the electricity market, coupled with the rising penetration of renewable energy sources, has spurred demand for enhanced load forecasting accuracy. Traditional single predictors exhibit limited robustness and diversity, rendering them unsuitable for most forecasting situations. Moreover, weather conditions, holidays, and social events significantly influence load demand, making forecasting techniques that account for these factors increasingly relevant. To bridge the gaps and further enhance predictive performance, in this paper, a novel adaptive multivariate combined forecasting system is proposed, incorporating redundant feature elimination, adaptive model selection, multi-objective intelligent optimization with non-negative constraints, and reliable interval prediction theory. The proposed system employs non-linear feature selection to identify effective load features and develops an advanced Multi-Objective Optimization Strategy (MOS) for combining multiple optimal predictors under various scenarios. The weight coefficients of sub-predictors are theoretically demonstrated to be Pareto optimal, and the MOS exhibits superior distribution and convergence. Simulation results reveal that the developed system substantially outperforms benchmark models, achieving reliable uncertainty quantification, which facilitates the rational allocation of spinning reserves and enhances dispatching efficiency.

BCNN: Backpropagation CNN-Based fully unsupervised skull stripping for accurate brain segmentation

Brain skull stripping is an essential step before performing the segmentation. It leads to better performance and less computational load on the model due to the elimination of redundant features from the image. However, the preparation of the skull-stripped ground truth brain images by the experts is a very tedious task and may lead to human errors. In this article, a fully unsupervised approach to brain extraction has been proposed. The cascaded loss function is used and is tuned for better segmentation. The number of connected components is also tuned in each type of dataset. The cascaded loss function is a combination of focal loss (FL) and dice loss (DL). To address the class imbalance issue, the leaky ReLU activation function is used. Enhancement of the brain image has been performed before extraction which yielded better performance. In comparison with other methods, the proposed backpropagation-based convolutional neural network (BCNN) work gives better qualitative and quantitative outcomes on four out of seven parameters. The dice similarity coefficient (DSC) of the proposed model is 0.89 which is the highest as compared to the other models for brain extraction. The specificity of the model is 0.998 with 97.21 % accuracy. The undersegmentation error is reduced to 1.2002 using the cascaded loss function. The proposed work has been evaluated on four brain image datasets. It has been found that the proposed model extracts the brain from the skull and also the white matter, gray matter, and cerebrospinal fluid. Therefore, it has been noted that the proposed model will be beneficial for efficient brain skull stripping without the availability of ground truth data.

Shrinkage and Redundant Feature Elimination Network-Based Robust Image Zero-Watermarking

To address the contradiction between watermarking robustness and imperceptibility, a zero-watermarking method based on shrinkage and a redundant feature elimination network (SRFENet) is proposed in this paper. First, in order to have the capability of resisting different image attacks, a dense connection was used to extract shallow and deep features from different convolutional layers. Secondly, to reduce unimportant information for robustness and uniqueness, in SRFENet, a shrinkage module was utilized by automatically learning the threshold of each feature channel. Then, to enhance watermarking uniqueness, a redundant feature elimination module was designed to reduce redundant information for the remaining valid features by learning the weights of inter-feature and intra-feature. In order to increase watermarking robustness further, noised images were generated for training. Finally, an extracted feature map from SRFENet was used to construct a zero-watermark. Furthermore, a zero-watermark from the noised image was generated for copyright verification, which is symmetrical to the process of zero-watermark construction from the original image. The experimental results showed that the proposed zero-watermarking method was robust to different single-image attacks (average BER is 0.0218) and hybrid image attacks (average NC is 0.9551), proving the significant generalization ability to resist different attacks. Compared with existing zero-watermarking methods, the proposed method is more robust since it extracts the main image features via learning a large number of different images for zero-watermark construction.

Measurement of Moisture in Transformer Insulation Using the Intelligent High-Frequency Sensor System

High-frequency sensor-based system is proposed for evaluating the degradation of the transformer insulation through the measurement of moisture concentration. The proposed system employs an intelligent open-ended coaxial probe microwave technique. An optimized ANN model is designed by selecting appropriate features to obtain accurate dielectric characterisation. Redundant feature elimination reduces the computational burden and also bypasses the repeated calibration refresh required to complete RF measurement in the conventional technique. ANN model is trained in such a way that measured impedance mismatch due to the termination by the sample at the probe end is related to its permittivity. The introduction of an intelligent standardization procedure led to a low error in the measurement of moisture (< ±3%) with a sensitivity of 0.027/ppm. This method is found to be simple and faster compared to other existing techniques. Due to the advantage of high-speed measurement and low error, this sensing technique is very suitable for transformer condition monitoring.

An efficient feature selection framework based on information theory for high dimensional data

Feature selection plays a vital role in many fields, particularly in pattern recognition and bioinformatics, for selecting informative and relevant features from high dimensional datasets. The increase in dimensionality of data along with the existence of redundant and irrelevant features leads to challenging performance issues when processing and analysing the data. In this paper, an effective feature selection technique called mutual information and Monte Carlo based feature selection (MIMCFS) is proposed. It comprises of two stages. The first stage aims to select predominant features from the high dimensional data. The second stage involves elimination of redundant features that were selected in the first stage. For the purpose of implementing the first stage, a new feature selection strategy based on the approximate Markov blanket and the concept of mutual information is proposed to find out irrelevant and redundant features. In second stage, to avoid misjudgement of redundant features as relevant features, a new strategy based on Monte Carlo tree search technique is proposed in order to completely eradicate redundant features and to improve feature interaction. For experimental evaluation, eight benchmark microarray datasets including imbalanced ones pertaining to cancer analysis are used. Further, in order to compare and justify the performance of the proposed feature selection method, seven state-of-art feature selection techniques namely CFS, Relief, DISR, JMI, CMIM and CMI are employed. The outputs from these feature selection techniques are provided to three standard classifiers namely Naive Bayes, SVM and C4.5 in order to assess the significance of the selected features in building classification models. 10-fold cross validation is adopted to evaluate the classifiers. Accuracy, precision, recall, f-measure, standard deviation, statistical significance metrics are measured to quantify the classifier performance. Experimental results demonstrate the outstanding performance of the proposed algorithm when compared to that of the standard existing methods.

Adsorption kinetics feature extraction from breathprint obtained by graphene based sensors for diabetes diagnosis

The correlation between blood glucose and breath acetone suggested by several studies has spurred the research community to develop an electronic (e-nose) for diabetes diagnosis. Herein, we have validated the in-house graphene based sensors with known acetone concentration. The sensor performances such as sensitivity, selectivity and stability (SSS) suggested their potential use in acquiring breath print. The 10% higher mean saturation voltage for 30 diabetic subjects ensured a discrimination accuracy of 65% with a positive correlation (r = 0.88) between biochemically measured and non-invasively estimated (glycated haemoglobin) HbA1c. For the improvement of classification rate, thirteen features associated with the adsorption kinetics were extracted from the breathprint from each of the three sensors. The features given as an input to the Naïve Bayes classification model fetched an accuracy of 68.33%. Elimination of redundant features by distinction index and one-R feature ranking algorithm results in Naïve Bayes algorithm with improved performances. The success rate has improved to 70% using the subset of features ranked by one-R algorithm. These results indicated the use of feature ranking algorithms and prediction models for the improvement in accuracy of our in-house fabricated graphene based sensors.

Research on Hybrid Feature Selection Method Based on Iterative Approximation Markov Blanket.

The basic experimental data of traditional Chinese medicine are generally obtained by high-performance liquid chromatography and mass spectrometry. The data often show the characteristics of high dimensionality and few samples, and there are many irrelevant features and redundant features in the data, which bring challenges to the in-depth exploration of Chinese medicine material information. A hybrid feature selection method based on iterative approximate Markov blanket (CI_AMB) is proposed in the paper. The method uses the maximum information coefficient to measure the correlation between features and target variables and achieves the purpose of filtering irrelevant features according to the evaluation criteria, firstly. The iterative approximation Markov blanket strategy analyzes the redundancy between features and implements the elimination of redundant features and then selects an effective feature subset finally. Comparative experiments using traditional Chinese medicine material basic experimental data and UCI's multiple public datasets show that the new method has a better advantage to select a small number of highly explanatory features, compared with Lasso, XGBoost, and the classic approximate Markov blanket method.

A new optimal feature selection scheme for classification of power quality disturbances based on ant colony framework

The optimal feature selection problem in classification of power quality disturbances is concerned with the elimination of redundant features. Its primary objective is to improve the classification accuracy and computational time. Limited literature on this topic and possible inefficiency in the existing works, makes it imperative to explore the new feature selection techniques. This paper, thus presents an ant colony optimization model for the same. A new multiobjective feature selection scheme is proposed which minimizes the product of feature set size and classification error. A feature set is declared feasible, if and only if it offers lesser error and features than other sets. The proposed scheme is compared against two other schemes from literature in same optimization framework. S-transform and time–time transform are employed for detection and feature extraction. This combination has a proven ability of offering precise time–frequency details and a wide range of features for optimal selection problem. Three classifiers, namely decision tree, k-nearest neighbour, and support vector machine are used to check the generality of proposed methodology. Disturbances simulated as per IEEE-1159 are used for initial testing, which confirm that the previous schemes may render accuracies even worse than those obtained without optimal feature selection. Conversely, the proposed scheme ensures a remarkable accuracy even when validated with real disturbances from an experimental database. A performance comparison with some recent works is also shown.

Machine health monitoring based on locally linear embedding with kernel sparse representation for neighborhood optimization

Abstract Manifold is considered to be a low dimensional surface embedded in a high dimensional vector space, and manifold learning is to find this surface based on data points sampled from this vector space. Neighborhood construction is a critical step in manifold learning to retain local relationship of data, i.e., neighbors and the connection weights. Current methods for manifold learning, including locally linear embedding, locality preserving projection, etc., assume fixed and linear neighborhood, thus lacking in adaptability for handling nonlinear system states caused by variations in machine condition or operation. To overcome this limitation, an enhanced manifold learning method is developed by utilizing kernel sparse representation to determine data neighbors and connecting weights. This enhanced manifold learning method maps data into a feature space where a kernel function is adopted to represent data by its neighbors nonlinearly. The number of data neighbors and connecting weights are determined adaptively by kernel sparse representation. It is found that the developed method enables state-related feature fusion and redundant feature elimination, thus is more effective for dimensionality reduction and feature extraction than traditional manifold learning. Analysis using vibration data measured on a gearbox with multiple faults of varying severity degrees confirmed the performance of the developed method.

A Rough Based Hybrid Binary PSO Algorithm for Flat Feature Selection and Classification in Gene Expression Data

Feature selection in high dimensional data, particularly, in gene expression data, is one of the challenging task in bioinformatics due to the curse of dimensionality, data redundancy and noise values. In gene expression data, insignificant features causes poor classification, hence feature selection reduces feature subset, improving classification accuracy. Feature selection algorithms in gene expression data(such as filter based, wrapper based and hybrid methods) performing poor accuracy, where as few methods takes too much time to converge for an acceptable results. For example, in NSGA-II, over 10,000 generations, on an average, to converge in the search space. where it incurs increased computational time. Proposed rough based hybrid binary PSO algorithm, which uses a heuristic based fast processing strategy to reduce crude domain features by statistical elimination of redundant features and then discretized subsequently into a binary table, known as distinction table, in rough set theory. This distinction table is later used as input to evaluate and optimize the objectives functions i.e., to generate reduct in rough set theory. The proposed hybrid binary PSO is then used to tune the objective functions, to choose the most important features (i:e:reduct). The fitness function is used in such a way that it can reduce the cardinality of the features and at the same time, improve the classification performance as well. Results have been demonstrated to show the effectiveness of the proposed method, on existing three benchmark datasets (i.e. colon cancer, lymphoma and leukemia data), from literature.

Combined VMD-SVM based feature selection method for classification of power quality events

Power quality (PQ) issues have become more important than before due to increased use of sensitive electrical loads. In this paper, a new hybrid algorithm is presented for PQ disturbances detection in electrical power systems. The proposed method is constructed based on four main steps: simulation of PQ events, extraction of features, selection of dominant features, and classification of selected features. By using two powerful signal processing tools, i.e. variational mode decomposition (VMD) and S-transform (ST), some potential features are extracted from different PQ events. VMD as a new tool decomposes signals into different modes and ST also analyzes signals in both time and frequency domains. In order to avoid large dimension of feature vector and obtain a detection scheme with optimum structure, sequential forward selection (SFS) and sequential backward selection (SBS) as wrapper based methods and Gram–Schmidt orthogonalization (GSO) based feature selection method as filter based method are used for elimination of redundant features. In the next step, PQ events are discriminated by support vector machines (SVMs) as classifier core. Obtained results of the extensive tests prove the satisfactory performance of the proposed method in terms of speed and accuracy even in noisy conditions. Moreover, the start and end points of PQ events can be detected with high precision.

Empirical Analysis of Supervised and Unsupervised Filter based Feature Selection Methods for Breast Cancer Classification from Digital Mammograms

In the design and development of an automated CAD tool for breast cancer detection and diagnosis, the various steps include enhancement, segmentation, feature extraction, feature selection and classification. The feature selection plays an important role in the design of the said CAD tool as it aims towards the redundant feature elimination and relevant feature selection. The selected feature set also decides the efficacy of the chosen classifier for classification of mammograms. In literature, various filter based feature selection methods exists under unsupervised and supervised categories based on different basis criterion. The filter based feature selection methods ranks the extracted feature sets based on some criteria in descending order of their importance. The various methods produce different feature subsets which are associated with different performance measures. In this paper, an evaluation and comparative study of various unsupervised and supervised feature selection methods are presented for breast cancer classification from digital mammograms though various classifiers. The study aims towards finding out the better feature selection method and associated classifier which gives better performance.

Syntactic features

The aim of this paper is to justify the use of syntactic features, to show how they relate to semantic features and how they operate in syntax and morphology. 1 Particular stress is laid on the elimination of redundant features. First we shall review the literature, to show why syntactic features should require justification. Then, as an illustration, we shall investigate the feature of animacy in Russian in all components of the grammar in which it has a rôle.