A Feature Subset Selection Technique  for High Dimensional Data Using  Symmetric Uncertainty

Singh, Bharat; Kushwaha, Nidhi; Vyas, O. P.

doi:10.4236/jdaip.2014.24012

Cited by 61 publications

(24 citation statements)

References 18 publications

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“…Symmetrical uncertainty (SU) is an information entropy-based filtering approach that measures the changes in entropy based on the concept of information entropy in order to assess the similarity or mutual information between the GCM and observed datasets [33,35]. The information entropy estimates the amount of information common between the two variables.…”

Section: Model Selection Using Symmetrical Uncertaintymentioning

confidence: 99%

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Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria

Hassan

Kalin

White

et al. 2020

Water

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Selection of a suitable general circulation model (GCM) ensemble is crucial for effective water resource management and reliable climate studies in developing countries with constraint in human and computational resources. A careful selection of a GCM subset by excluding those with limited similarity to the observed climate from the existing pool of GCMs developed by different modeling centers at various resolutions can ease the task and minimize uncertainties. In this study, a feature selection method known as symmetrical uncertainty (SU) was employed to assess the performance of 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM outputs under Representative Concentration Pathway (RCP) 4.5 and 8.5. The selection was made according to their capability to simulate observed daily precipitation (prcp), maximum and minimum temperature (Tmax and Tmin) over the historical period 1980-2005 in the Niger Delta region, which is highly vulnerable to extreme climate events. The ensemble of the four top-ranked GCMs, namely ACCESS1.3, MIROC-ESM, MIROC-ESM-CHM, and NorESM1-M, were selected for the spatio-temporal projection of prcp, Tmax, and Tmin over the study area. Results from the chosen ensemble predicted an increase in the mean annual prcp between the range of 0.26% to 3.57% under RCP4.5, and 0.7% to 4.94% under RCP 8.5 by the end of the century when compared to the base period. The study also revealed an increase in Tmax in the range of 0 to 0.4 • C under RCP4.5 and 1.25-1.79 • C under RCP8.5 during the periods 2070-2099. Tmin also revealed a significant increase of 0 to 0.52 • C under RCP4.5 and between 1.38-2.02 • C under RCP8.5, which shows that extreme events might threaten the Niger Delta due to climate change. Water resource managers in the region can use these findings for effective water resource planning, management, and adaptation measures.

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Section: Model Selection Using Symmetrical Uncertaintymentioning

confidence: 99%

“…The information entropy estimates the amount of information common between the two variables. For example, if P(X) and P(Y) are the probability density functions and P(X, Y) is the joint probability density function of A and B, then the entropy H between X and Y is given in Equation (1) below [35,52]:…”

Section: Model Selection Using Symmetrical Uncertaintymentioning

confidence: 99%

Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria

Hassan

Kalin

White

et al. 2020

Water

View full text Add to dashboard Cite

show abstract

“…F-correlation: the SU between any pair of features F i and F j (i = j), denoted by SU i,j . The use of symmetrical uncertainty has been proved to be useful in dimensionality reduction in previous studies [24][25][26][27][28][29].…”

Section: Symmetrical Uncertaintymentioning

confidence: 99%

Symmetrical Uncertainty-Based Feature Subset Generation and Ensemble Learning for Electricity Customer Classification

Piao

Lee

2019

Symmetry

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The use of actual electricity consumption data provided the chance to detect the change of customer class types. This work could be done by using classification techniques. However, there are several challenges in computational techniques. The most important one is to efficiently handle a large number of dimensions to increase customer classification performance. In this paper, we proposed a symmetrical uncertainty based feature subset generation and ensemble learning method for the electricity customer classification. Redundant and significant feature sets are generated according to symmetrical uncertainty. After that, a classifier ensemble is built based on significant feature sets and the results are combined for the final decision. The results show that the proposed method can efficiently find useful feature subsets and improve classification performance.

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“…To find the key factors that affect load patterns, the filter models [21] work the best among existing feature selection methods. Specifically, the entropy-based class measurement [22], which has been widely employed in filter models, is utilized in our study.…”

Section: A Socioeconomic Features Selectionmentioning

confidence: 99%

Leveraging Socioeconomic Information and Deep Learning for Residential Load Pattern Prediction

Tang

Lee

Wang

et al. 2019

2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

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Advanced metering infrastructure systems record a high volume of residential load data, opening up an opportunity for utilities to understand consumer energy consumption behaviors. Existing studies have focused on load profiling and prediction, but neglected the role of socioeconomic characteristics of consumers in their energy consumption behaviors. In this paper, we develop a prediction model using deep neural networks to predict load patterns of consumers based on their socioeconomic information. We analyze load patterns using the K-means clustering method and use an entropy-based feature selection method to select the key socioeconomic characteristics that affect consumers' load patterns. Our prediction method with feature selection achieves a higher prediction accuracy compared with the benchmark schemes, e.g. 80% reduction in the prediction error.

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A Feature Subset Selection Technique for High Dimensional Data Using Symmetric Uncertainty

Cited by 61 publications

References 18 publications

Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria

Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria

Symmetrical Uncertainty-Based Feature Subset Generation and Ensemble Learning for Electricity Customer Classification

Leveraging Socioeconomic Information and Deep Learning for Residential Load Pattern Prediction

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