Minimum Redundancy Criterion Research Articles

Customer characteristics analysis method based on the selection of electricity consumption characteristics and behavioral portraits of different groups of people

Because the traditional methods do not select the best feature collection in feature analysis, the accuracy and effectiveness of user feature clustering are not high, and the accuracy of user feature classification is not high. Therefore, this paper proposes a customer feature analysis method based on power consumption feature selection and behavior portrait of different people. The optimal feature set is obtained according to the maximum correlation and minimum redundancy criterion, and the user portrait task is described. The spatial feature domain classification method is used to classify the user portrait information, and the user label database is constructed according to the classification results. The AP clustering algorithm is used to cluster the power user portrait information and complete the customer feature analysis. Experimental results show that this method effectively improves the accuracy and effectiveness of user feature clustering, and the accuracy of user feature classification is high, indicating that the application effect is good.

Semi−Supervised Hybrid Modeling of the Yeast Fermentation Process

This study focuses on modeling the yeast fermentation process using the hybrid modeling method. To improve the prediction accuracy of the model and reduce the model training time, this paper presents a semi−supervised hybrid modeling method based on an extreme learning machine for the yeast fermentation process. The hybrid model is composed of the mechanism model and the residual model. The residual model is built from the residuals between the real yeast fermentation process and the mechanism model. The residual model is used in parallel with the mechanism model. Considering that the residuals might be related to the inaccurate parameters or structure of the process, the mechanism model output is taken as unlabeled data, and the suitable inputs are selected based on Pearson’s maximum correlation and minimum redundancy criterion (RRPC). Meanwhile, an extreme learning machine is employed to improve the model’s training speed while maintaining the model’s prediction accuracy. Consequently, the proposal proved its efficacy through simulation.

A hybrid day-ahead electricity price forecasting framework based on time series

Electricity price forecasting (EPF) plays an indispensable role in the decision-making processes of electricity market participants. However, the complexity of electricity markets has made EPF increasingly difficult. Currently, popular methods for EPF are based on signal decomposition and suffer from computational redundancy and hyperparameter optimization challenges. In this paper, we propose a new hybrid forecasting framework to improve the forecasting accuracy of day-ahead electricity prices. The proposed model consists of three valuable strategies. First, an adaptive copula-based feature selection (ACBFS) algorithm based on the maximum correlation minimum redundancy criterion is proposed for selecting model input features. Second, a new method of signal decomposition technique for EPF field is proposed based on decomposition denoising strategy. Third, a Bayesian optimization and hyperband (BOHB) optimized long short-term memory (LSTM) model is used to improve the effect of hyperparameter settings on the prediction results. The effectiveness of the different techniques was broadly cross-validated using five datasets set up for the PJM electricity market, and the results indicated that the proposed hybrid algorithm is more effective and practical for day-ahead EPF.

Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith.

To investigate the clinical application of the three-dimensional (3D) radiomics model of the CT image in the diagnosis and identification of ureteral calculus and phlebolith. Sixty-one cases of ureteral calculus and 61 cases of phlebolith were retrospectively investigated. The enrolled patients were randomly categorized into the training set (n = 86) and the testing set (n = 36) with a ratio of 7 : 3. The plain CT scan images of all samples were manually segmented by the ITK-SNAP software, followed by radiomics analysis through the Analysis Kit software. A total of 1316 texture features were extracted. Then, the maximum correlation minimum redundancy criterion and the least absolute shrinkage and selection operator algorithm were used for texture feature selection. The feature subset with the most predictability was selected to establish the 3D radiomics model. The performance of the model was evaluated by the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) was also calculated. Additionally, the decision curve was used to evaluate the clinical application of the model. The 10 selected radiomics features were significantly related to the identification and diagnosis of ureteral calculus and phlebolith. The radiomics model showed good identification efficiency for ureteral calculus and phlebolith in the training set (AUC = 0.98; 95%CI: 0.96-1.00) and testing set (AUC = 0.98; 95%CI: 0.95-1.00). The decision curve thus demonstrated the clinical application of the radiomics model. The 3D radiomics model based on plain CT scan images indicated good performance in the identification and prediction of ureteral calculus and phlebolith and was expected to provide an effective detection method for clinical diagnosis.

An Integrated Terrain Identification Framework for Mobile Robots: System Development, Analysis, and Verification

Terrain identification is essential to autonomous control algorithm development for mobile robots. This article proposes an integrated framework to identify terrain parameters based on inertial, and driving current signals. Multiple sources are combined to reduce the instability caused by single signals. A dynamic model of the track-soil system is established as the theoretical basis of identification. All signals are processed in the time, frequency, and time-frequency domains. The features of each domain are generated by statistical methods. To analyze, and select superior feature categories, a maximum-relevance, and minimum-redundancy criterion based on Pearson's correlation is proposed to evaluate the priority of features. A probabilistic neural network is used to identify the category of terrain. All results are analyzed with two factors, source, and input, to find the most effective rule of the proposed framework. The crossing combination analysis is taken into consideration to explore all potential improvement. The results show that the driving current yields comparative identification accuracy as inertial signals. Compared to the single signal source, the method using the combined signal source can effectively improve the accuracy of terrain identification.

Network anomaly detection based on selective ensemble algorithm

In order to reduce the loss of information of the majority class samples in the resampling process, combining the distribution of class samples and the characteristics of ensemble learning algorithm, in this paper, a two-level selective ensemble learning algorithm for imbalanced datasets is proposed. Firstly, the algorithm under-samples the majority class samples and constructs multiple training subsets. The training process will generate multiple base classifiers using AdaBoost algorithm, then select some base classifiers according to maximum correlation and minimum redundancy criteria, and form sub-classifiers according to weighted integration. Then, generate multiple sub-classifiers for multiple training subsets, and then, select some sub-classifiers according to maximum correlation and minimum redundancy criteria. Then, the weights of the selected sub-classifiers are calculated by F-means or G-means, and the ensemble classifier is obtained by weighted voting. Finally, the improved algorithm for imbalanced dataset is applied to the network anomaly detection. The experimental results on UCI datasets show that this method can improve the classification performance to a certain extent, especially for imbalanced datasets. Finally, the algorithm is applied to network anomaly detection for Internet of Things. From the simulation data of KDDCUP99 dataset, we can see that TLSE-ID algorithm has a small missing report rate and high precision.

Partial discharge feature extraction based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy

The recognition of partial discharge mode is an important indicator of the insulation condition in transformers, based on which maintenance can be arranged. Discharge feature extraction is the key to recognize discharge mode. To solve the problem of poor stability and low recognition rate of partial discharge mode, this paper proposes a feature extraction method based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy. First, the four partial discharge signals collected under laboratory conditions are decomposed by synchrosqueezed windowed Fourier transform, then a number of band-limited intrinsic mode type functions are obtained, and the original feature quantities of partial discharge signals are obtained by calculating the multi-scale dispersion entropies of each intrinsic mode type function. Based on that, original feature quantity is optimized by using the maximum relevance and minimum redundancy criteria. Finally, the classification is implemented by the support vector machine. Experimental results show that in the case of noise interference, the proposed synchrosqueezed windowed Fourier transform–multi-scale dispersion entropy method can still accurately describe the feature of different discharge signals and has a higher recognition rate than both the empirical mode decomposition–multi-scale dispersion entropy method and the direct multi-scale dispersion entropy method.

SF6 fault decomposition feature component extraction and triangle fault diagnosis method

How to use SF 6 decomposition feature component information to judge the form and degree of gas-insulated equipment (GIE) field discharge and overthermal faults quickly is a problem that remains unresolved. Based on the existing experimental data on SF 6 typical fault decomposition, this study considers the SF 6 decomposition mechanism under typical faults and uses the maximum correlation minimum redundancy criterion to filter out three decomposition feature components characterizing GIE typical fault attributes: SOF 2 +SO 2 , CF 4 , and SO 2 F 2 . The weight of extracted feature components is optimized by the area equivalence principle, and the triangle fault diagnosis method of the SF 6 decomposition component that is suitable for the rapid diagnosis of a GIE field is constructed. The diagnosis method is comprehensively tested using faulty data in different conditions, and the comprehensive recognition rate of lab tests reaches 96.2%. Results show that the constructed triangle fault diagnosis method of the SF 6 decomposition component can diagnose the internal fault nature of a GIE and identify the types of insulation defects that induce partial discharge faults. Moreover, the constructed method in this research is simple, effective, and suitable for field maintenance and online intelligent monitoring of GIE.

Minimum redundancy maximum relevance feature selection approach for temporal gene expression data

BackgroundFeature selection, aiming to identify a subset of features among a possibly large set of features that are relevant for predicting a response, is an important preprocessing step in machine learning. In gene expression studies this is not a trivial task for several reasons, including potential temporal character of data. However, most feature selection approaches developed for microarray data cannot handle multivariate temporal data without previous data flattening, which results in loss of temporal information.We propose a temporal minimum redundancy - maximum relevance (TMRMR) feature selection approach, which is able to handle multivariate temporal data without previous data flattening. In the proposed approach we compute relevance of a gene by averaging F-statistic values calculated across individual time steps, and we compute redundancy between genes by using a dynamical time warping approach.ResultsThe proposed method is evaluated on three temporal gene expression datasets from human viral challenge studies. Obtained results show that the proposed method outperforms alternatives widely used in gene expression studies. In particular, the proposed method achieved improvement in accuracy in 34 out of 54 experiments, while the other methods outperformed it in no more than 4 experiments.ConclusionWe developed a filter-based feature selection method for temporal gene expression data based on maximum relevance and minimum redundancy criteria. The proposed method incorporates temporal information by combining relevance, which is calculated as an average F-statistic value across different time steps, with redundancy, which is calculated by employing dynamical time warping approach. As evident in our experiments, incorporating the temporal information into the feature selection process leads to selection of more discriminative features.

AlPOs Synthetic Factor Analysis Based on Maximum Weight and Minimum Redundancy Feature Selection

The relationship between synthetic factors and the resulting structures is critical for rational synthesis of zeolites and related microporous materials. In this paper, we develop a new feature selection method for synthetic factor analysis of (6,12)-ring-containing microporous aluminophosphates (AlPOs). The proposed method is based on a maximum weight and minimum redundancy criterion. With the proposed method, we can select the feature subset in which the features are most relevant to the synthetic structure while the redundancy among these selected features is minimal. Based on the database of AlPO synthesis, we use (6,12)-ring-containing AlPOs as the target class and incorporate 21 synthetic factors including gel composition, solvent and organic template to predict the formation of (6,12)-ring-containing microporous aluminophosphates (AlPOs). From these 21 features, 12 selected features are deemed as the optimized features to distinguish (6,12)-ring-containing AlPOs from other AlPOs without such rings. The prediction model achieves a classification accuracy rate of 91.12% using the optimal feature subset. Comprehensive experiments demonstrate the effectiveness of the proposed algorithm, and deep analysis is given for the synthetic factors selected by the proposed method.

A NOVEL FEATURE SELECTION ALGORITHM WITH SUPERVISED MUTUAL INFORMATION FOR CLASSIFICATION

Feature selection is essential in data mining and pattern recognition, especially for database classification. During past years, several feature selection algorithms have been proposed to measure the relevance of various features to each class. A suitable feature selection algorithm normally maximizes the relevancy and minimizes the redundancy of the selected features. The mutual information measure can successfully estimate the dependency of features on the entire sampling space, but it cannot exactly represent the redundancies among features. In this paper, a novel feature selection algorithm is proposed based on maximum relevance and minimum redundancy criterion. The mutual information is used to measure the relevancy of each feature with class variable and calculate the redundancy by utilizing the relationship between candidate features, selected features and class variables. The effectiveness is tested with ten benchmarked datasets available in UCI Machine Learning Repository. The experimental results show better performance when compared with some existing algorithms.

Predicting atrial fibrillation inducibility in a canine model by multi-threshold spectra of the recurrence complex network

The purpose of this study is to predict atrial fibrillation (AF) from epicardial signals by investigating the recurrence property of atrial activity dynamic system before AF. A novel scheme is proposed to predict AF by using multi-threshold spectra of the recurrence complex network. Firstly, epicardial signals are transformed into the recurrence complex network to quantify structural properties of the recurrence in the phase space. Spectral parameters with multi-threshold are used to characterize the global structure of the network. Then the feature sequential forward searching algorithm and mutual information based Maximum Relevance Minimum Redundancy criterion are used to find the optimal feature set. Finally, a support vector machine is used to predict the occurrence of AF. This method is assessed on the pre-AF epicardial signals of canine which includes the normal group A (no further AF will happen), the mild group B (the following AF time is less than 180s) and the severe group C (the following AF time is more than 180s). 25 optimal features are selected out of 180 features from each sample. With these features, sensitivity, specificity and accuracy are 99.40%, 99.70% and 99.60%, respectively, which are the best among the recurrence based methods. The results suggest that the proposed method can predict AF accurately and thus can be prospectively used in the postoperative evaluation.

A Cell Profiling Framework for Modeling Drug Responses from HCS Imaging

The authors present an unsupervised, scalable, and interpretable cell profiling framework that is compatible with data gathered from high-content screening. They demonstrate the effectiveness of their framework by modeling drug differential effects of IC-21 macrophages treated with microtubule and actin disrupting drugs. They identify significant features of cell phenotypes for unsupervised learning based on maximum relevancy and minimum redundancy criteria. A 2-stage clustering approach annotates, clusters cells, and then merges them together to form super-clusters. An interpretable cell profile consisting of super-cluster proportions profiled at each drug treatment, concentration, or duration is obtained. Differential changes in super-cluster profiles are the basis for understanding the drug's differential effect and biology. The authors' method is validated by significant chi-squared statistics obtained from similar drug-treated super-cluster profiles from a 5-fold cross-validation. In addition, drug profiles of 2 microtubule drugs with equivalent mechanisms of action are statistically similar. Several distinct trends are identified for the 5 cytoskeletal drugs profiled under different conditions.

Reconstructing attractors from scalar time series: A comparison of singular system and redundancy criteria

A delay-vector phase space reconstruction in which the delay time satisfiesa minimum redundancy criterion is compared with a reconstruction obtained using a singular system approach. Minimum redundancy produces the better reconstruction. The reconstructions are compared using a distortion functional D which measures how well the location of a point in the original phase space can be determined on the basis of its image under the reconstruction process. The superiority of the redundancy analysis over the singular system analysis is found to arise from the former's foundation on the notion of general independence as opposed to the latter's foundation on the notion of linear independence.