Correlation Review of Classification Algorithm Using Data Mining Tool: WEKA, Rapidminer, Tanagra, Orange and Knime

Naik, Amrita; Samant, Lilavati

doi:10.1016/j.procs.2016.05.251

Cited by 116 publications

(65 citation statements)

References 2 publications

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“…The traditional statistical cluster analysis methods include systematic clustering, dynamic clustering, rough set and fuzzy clustering. The classification process refers to use the classification function or model based on given sample data to determine the new data type [18]. The key of classification model is to build the classifier and to determine classification function.…”

Section: Classification Of Aml Recognition Methods Based On Data Miningmentioning

confidence: 99%

Recognition Technology of Money Laundering Transactions Based on Data Mining

Wu¹

2018

dtem

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Section: Classification Of Aml Recognition Methods Based On Data Miningmentioning

confidence: 99%

Recognition Technology of Money Laundering Transactions Based on Data Mining

Wu¹

2018

dtem

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“…The data contained in the databases could be used to learn a specific target concept [35][36][37][38]. The tasks performed by data mining techniques and machine learning, the classification, build models that can be applied to unclassified data to categorize them into classes, to relate the meta attribute (whose value will be predicted) and a set of forecasting attributes [35][36][37][38].…”

Section: Classification Of Model Predictionmentioning

confidence: 99%

Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

et al. 2020

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The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to predict the models. The classification of the E-EER scores using a decision tree using the J48 algorithm and Bayesian classifiers using the naive Bayes algorithm produced decision tree models efficient at estimating the efficiency level of Brazilian ethanol producers and importers certified by the RenovaBio. The rules generated by the models can assess the level classes (efficiency scores) according to the scale discretized into high efficiency (Classification A), average efficiency (Classification B), and standard efficiency (Classification C). These results might generate an ethanol energy-environmental efficiency label for the end consumers and resellers of the product, to assist in making a purchase decision concerning its performance. The best classification model was naive Bayes, compared to the J48 decision tree. The classification of the Energy Efficiency Note levels using the naive Bayes algorithm produced a model capable of estimating the efficiency level of Brazilian ethanol to create labels.The production of biofuel from sugarcane is supported by the National Agency of Petroleum, Natural Gas and Biofuels' (ANP) sustainable development programs to achieve goals to reduce greenhouse gas emissions. Therefore, annual national targets were developed by the Brazilian National Biofuel Policy (RenovaBio) [12,15]. These goals are established in units of Decarbonization Credits (CBios) calculated from annual mandatory targets defined individually for each producer and distributor according to their participation in the fossil fuel market [12,16,[18][19][20][21].The certification of biofuel production assigns different marks to each biofuel producer and importer, in a value inversely related to the carbon amount of biofuel produced. The note reflects the individual contribution of each producing agent to mitigate a specific amount of GHG concerning its fossil substitute (in tons of CO 2 equivalent). The total emission is compared with that of the equivalent fossil fuel (gasoline, for ethanol), resulting in a final score (Energy-Environmental Efficiency Rating), characterizing the mitigation of emissions. This note generates CBios for biofuel producers and importers; with the decarbonization of the Brazilian energy matrix, there is a mechanism for the commercialization of these CBios linked to the carbon intensity of biofuels [2,12,16,18].The incentive to reduce pollutant emissions in the biofuel chain goes far beyond the use o...

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“…It contains a large collection of state-of-the-art machine-learning and data-mining algorithms written in Java. WEKA has been widely used for many purposes and contains tools for regression, classification, clustering, association rules, visualization, and data preprocessing (Naik & Samant, 2016). The Explorer is the main graphical user interface of WEKA.…”

Section: Construction Of the Fuel Consumption Rate Modelmentioning

confidence: 99%

K-Nearest Neighbors Method for Prediction of Fuel Consumption in Tractor-Chisel Plow Systems

2019

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Most important farm operations require a significant amount of energy, and this consumes a major portion of the farm's budget. Consequently, analyzing the fuel consumption of agricultural machinery for farm operations of different sizes makes it possible to predict fuel consumption to set an appropriate budget for energy. The main purpose of this study was to determine the ability of the k-nearest neighbors (KNN) algorithm to predict the fuel consumption of tractor-chisel plow systems correctly. A training-set design of 139 points of 173 data points obtained from the literature was utilized, and the remaining 34 data points were applied as a test set. The input parameters were tractor power, plowing width, depth and speed of plowing, soil percentages of sand, silt, and clay, initial soil moisture content, and initial soil bulk density. The predictive power of the KNN method was compared with that of multiple linear regression (MLR), and experimental data were used to determine the predictive power of both methods. The KNN method generated better results than the multiple linear regression method. The test dataset correlation coefficients were 0.817 for the KNN (k = 2) method and 0.422 for the multiple linear regression method. This study suggests that the KNN method with k = 2 (two nearest neighbors) is suitable for estimating the fuel consumption of tractor-chisel plow systems for input values within the studied range.

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Correlation Review of Classification Algorithm Using Data Mining Tool: WEKA, Rapidminer, Tanagra, Orange and Knime

Cited by 116 publications

References 2 publications

Recognition Technology of Money Laundering Transactions Based on Data Mining

Recognition Technology of Money Laundering Transactions Based on Data Mining

Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

K-Nearest Neighbors Method for Prediction of Fuel Consumption in Tractor-Chisel Plow Systems

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