Comparison of Classification Techniques on Energy Efficiency Dataset

Abstract

Abstract:The definition of the data mining can be told as to extract information or knowledge from large volumes of data. Statistical and machine learning techniques are used for the determination of the models to be used for data mining predictions. Today, data mining is used in many different areas such as science and engineering, health, commerce, shopping, banking and finance, education and internet. This study make use of WEKA (Waikato Environment for Knowledge Analysis) to compare the different classification techniques on energy efficiency datasets. In this study 10 different Data Mining methods namely Bagging, Decorate, Rotation Forest, J48, NNge, K-Star, Naïve Bayes, Dagging, Bayes Net and JRip classification methods were applied on energy efficiency dataset that were taken from UCI Machine Learning Repository. When comparing the performances of algorithms it's been found that Rotation Forest has highest accuracy whereas Dagging had the worst accuracy.

A systematic review of big data in energy analytics using energy computing techniques

“…In 2017, Toprak et al 31 investigated diverse arrangement strategies on vitality proficiency datasets. In this investigation, 10 diverse data mining methods collectively known as the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) namely JRip, dagging, Naïve Bayes, non-nested generalization (NNge), J48, K-star, decorate, bayes net, rotation forest, and bagging grouping methods were applied on vitality productivity dataset that was taken from UCI machine learning repository.…”

An Octahedric Regression Model of Energy Efficiency on Residential Buildings

“…Artificial Neural Networks (ANN) [2,[9][10][11][12][13][14][15][16][17][18][19]] Decision Trees (DT) [20] Support Vector Regression (SVR) [11,13,14,19,[21][22][23]] Random Forest (RF) and Trees Ensemble [8,13,14,[20][21][22][23][24][25][26][27][28][29] Multi-Layer Perceptron (MLP) [14,20,23,27] Gaussian Mixture Model (GMM) [30] Gradient Boosted Regression Trees (GBRT) [24,31] Extreme Learning Machine (ELM) [10,32,33] Linear Regression (LR) [10,13,21,23,27,34,35] Radial Basis ...…”

“…Selection of variables have been done, by example, in [28], where different models are compared for the sets of variables (roof area, overall height), (relative compactness, roof area, overall height), (relative compactness, surface area, wall area, roof area, overall height, glazing area) and the full dataset. In reference [18], the considered variables are (surface area, wall area, roof area, overall height, glazing area), while reference [35] uses (relative compactness, surface area, wall area, overall height, glazing area) to model the cooling problem.…”