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\title{Comparative Analysis of Random Forest and J48 Classifiers for "IRIS" Variety Prediction}
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             \author[1]{Youness  Lakhdoura}

             \author[2]{Rachid  Elayachi}

             \affil[1]{  Sultan Moulay Slimane University}

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\date{\small \em Received: 10 December 2019 Accepted: 5 January 2020 Published: 15 January 2020}

\maketitle


\begin{abstract}
        


Data mining may be a computerized technology that uses complicated algorithms to seek out relationships and trends in large databases, real or perceived, previously unknown to the retailer, to market decision support. Data mining is predicted to be one of the widespread recognition of the potential for analysis of past transaction data to enhance the standard of future business decisions. The aim is to arrange a set of knowledge items and classify them.In this paper, we apply two classifier algorithms: J48 (c4.5) and Random Forest on the IRIS dataset, and we compare their performance based on different measures.

\end{abstract}


\keywords{IRIS, J48 classifier, proficiency comparison, random forest classifier}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
eople are often susceptible to making mistakes during analyses or, possibly, when trying to determine relationships between multiple features. This fact, makes it difficult for them to seek out solutions to certain problems. Data mining involves the utilization of sophisticated data analysis tools to get previously unknown, valid patterns, and relationships in the datasets \hyperref[b0]{[1]}. These tools can include statistical models, mathematical algorithms, and machine learning methods \hyperref[b1]{[2]}.\par
Consequently, data processing consists of quite a collection and managing data, it also includes analysis and prediction \hyperref[b0]{[1]}.\par
The classification technique is capable of processing a sort of data than regression and is growing in popularity \hyperref[b2]{[3]}. 
\section[{II.}]{II.} 
\section[{Dataset Used}]{Dataset Used}\par
In this research work, we use the IRIS plant data set, one of the most popular databases for the classification problems, it is obtained from UCI Machine Learning Repository and created by R.A. Fisher while donated by Michael Marshall (MARSHALL\%PLU @io.arc.nasa.gov) on  {\ref July 1988[4]}.\par
The IRIS dataset contains three different classes of IRIS plants depending on their pattern  {\ref [5,} {\ref 6]}. Each class of IRIS plant contain fifty objects. The attributes that already predicted belongs to a category of IRIS plant. The list of attributes presents within the IRIS is often described as categorical, nominal, and continuous. The experts have mentioned that the info set is complete i.e. there isn't any missing value found in any attribute of this data set  {\ref [6]}.\par
This research makes use of the documented IRIS dataset, which contains three classes of fifty instances each. The 150 instances, which are equally divided between the three classes, hold the subsequent four numeric attributes:  
\section[{Classifiers Used}]{Classifiers Used}\par
In this paper, we compared the proficiency assessment of IRIS variety for two tree based classifiers: Random Forest and J48 Classifiers. 
\section[{a) Random Forest Classifier}]{a) Random Forest Classifier}\par
Random Forest [7] is considered one of the best "off-the-shelf" classifiers for high-dimensional data. Random forest is a mix of tree predictors sampled autonomously count on the values of a random vector following an equivalent distribution for all trees of the forest. The generalization error of random forest classifier depends on the association between the individual trees inside the forest and the strength of them. The dataset divided into a training dataset to learn each tree, and the remaining of the data set is used to estimate error and variable importance. Class assignment is formed according to the number of votes for any of the trees, to apply the model of the results. it's almost like bagged decision trees with hardly some key differences as given below:\par
For every split point, the search isn't overall p variables but just over m (number of tested) variables (where, e.g,m = [p/3])\par
No pruning necessary. Trees are often grown until each node contains just only a few observations. The Random Forest gave better prediction, and almost no parameter adjustment is necessary. 
\section[{b) J48 Classifier}]{b) J48 Classifier}\par
The J48 classifier is an extension of the decision tree C4.5 algorithm for classification  {\ref [8]}, which creates a binary tree. It's the foremost useful decision tree approach for classification problems. This system constructs a tree to model the classification process. After the tree is made, the algorithm is applied to every tuple within the database and leads to classification for that tuple  {\ref [} The absent values are ignored byJ48 while building a decision tree, i.e. the known information about the attribute values for the other records is helpful to predict the value for that item. The idea is to divide the data into a range based on the attribute values for that element which are identified in the training sample  {\ref [10]}.\par
IV. 
\section[{Performance Measures Used}]{Performance Measures Used}\par
Various scales are wont to gauge the performance of the classifiers. 
\section[{a) Classification Accuracy (CA)}]{a) Classification Accuracy (CA)}\par
Classification accuracy presents the percent of correctly classified instance in the test dataset. We calculate it by dividing the correctly classified instances by the total number of instance multiplied by 100. 
\section[{b) Mean Absolute Error (MAE)}]{b) Mean Absolute Error (MAE)}\par
Mean absolute error is that the average of the variance between predicted and actual value altogether test cases. It's an honest measure to measure performance. 
\section[{c) Root Mean Square Error (RMSE)}]{c) Root Mean Square Error (RMSE)}\par
Root mean squared error is employed to scale dissimilarities between values. It's determined by taking the root of the mean square error. 
\section[{d) Confusion Matrix (CM)}]{d) Confusion Matrix (CM)}\par
A confusion matrix is a tool checking in particular how often the predictions are correct compared to reality in classification problems.\par
V. 
\section[{Results and Discussion}]{Results and Discussion}\par
In this work, to evaluate the performance of the different Tree-based Classifiers (Random Forest and J48), we used a well-known open-source tool in the machine learning field called "WEKA". The performance is tested using two methods, first by splitting the dataset into training (70\%) and testing (30\%) datasets, as well as using different Cross-Validation methods. 
\section[{a) Performance of Random Forest Classifier}]{a) Performance of Random Forest Classifier}\par
Table \hyperref[tab_2]{1} show the global evaluation summary of Random Forest Classifier using both of the test modes: splitting and different cross-validation methods. Fig.  {\ref 1} and Fig. \hyperref[fig_0]{2} display the performance of Random Forest Classifier in terms of Classification Accuracy and time taken to build the model. From Table  {\ref I} to Table VI we gave the confusion matrix for different test modes.\par
By applying these test modes using Random Forest Classifier, we got 95.55\% accuracy, spending 0.17s on building the model for the split. Using different cross-validation methods to check their performance, we obtained around 94.99\% accuracy, spending 0.06s on building the model.   By applying these test modes using J48classifier we got 95.55\% accuracy, spending 0.05s on building the model for the split mode. Using different cross-validation methods to check their performance, on average we obtained around 95.83\% accuracy, spending 0.025s to build the model.    Comparison of Random Forest and j48 Classifiers    
\section[{Conclusion}]{Conclusion}\par
This research work compares the efficiency of Random Forest and J48 Classifiers for IRIS variety prediction. The test is accomplished using WEKA 3.9in a machine with a processor i5-2430M 2.40 GHz and 4.00GB in RAM. Also, we compare the performance of both of the classifiers in terms of different scales of effectiveness evaluation. At last, we observed that J48classifier performs best than Random Forest classifier for IRIS variety prediction by taking different measures, including classification accuracy, Mean Absolute Error, and Time Taken to Build the Model.\begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Fig. 5}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Figure 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6}\includegraphics[]{image-7.png}
\caption{\label{fig_5}Figure 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.15\textwidth}P{0.11052631578947368\textwidth}P{0.09210526315789473\textwidth}P{0.11315789473684211\textwidth}P{0.12105263157894737\textwidth}P{0.13421052631578947\textwidth}P{0.12894736842105264\textwidth}}
\tabcellsep Correctly\tabcellsep Incorrectly\tabcellsep \tabcellsep Mean\tabcellsep Root Mean\tabcellsep Time Taken to\\
Test Mode\tabcellsep Classified\tabcellsep Classified\tabcellsep Accuracy\tabcellsep Absolute\tabcellsep Squared\tabcellsep Build Model\\
\tabcellsep Instances\tabcellsep Instances\tabcellsep \tabcellsep Error\tabcellsep Error\tabcellsep (Sec)\\
Split (70\%)\tabcellsep 43\tabcellsep 2\tabcellsep 95.55\%\tabcellsep 0.0363\tabcellsep 0.1532\tabcellsep 0.17\\
5 Fold CV\tabcellsep 143\tabcellsep 7\tabcellsep 95.33\%\tabcellsep 0.037\tabcellsep 0.1531\tabcellsep 0.05\\
10Fold CV\tabcellsep 142\tabcellsep 8\tabcellsep 94.66\%\tabcellsep 0.0408\tabcellsep 0.1624\tabcellsep 0.03\\
15Fold CV\tabcellsep 142\tabcellsep 8\tabcellsep 94.66\%\tabcellsep 0.0385\tabcellsep 0.1613\tabcellsep 0.14\\
20Fold CV\tabcellsep 143\tabcellsep 7\tabcellsep 95.33\%\tabcellsep 0.0379\tabcellsep 0.1558\tabcellsep 0.03\end{longtable} \par
 
\caption{\label{tab_2}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.3336448598130841\textwidth}P{0.09532710280373831\textwidth}P{0.12710280373831775\textwidth}P{0.11915887850467288\textwidth}P{0.1747663551401869\textwidth}}
\tabcellsep Setosa\tabcellsep Versicolor\tabcellsep Virginica\tabcellsep Actual (Total)\\
Setosa\tabcellsep 14\tabcellsep 0\tabcellsep 0\tabcellsep 14\\
Versicolor\tabcellsep 0\tabcellsep 16\tabcellsep 0\tabcellsep 16\\
Virginica\tabcellsep 0\tabcellsep 2\tabcellsep 13\tabcellsep 15\\
Predicted (Total)\tabcellsep 14\tabcellsep 18\tabcellsep 13\tabcellsep 45\end{longtable} \par
 
\caption{\label{tab_3}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.33055555555555555\textwidth}P{0.09444444444444444\textwidth}P{0.1259259259259259\textwidth}P{0.11805555555555555\textwidth}P{0.18101851851851852\textwidth}}
\tabcellsep Setosa\tabcellsep Versicolor\tabcellsep Virginica\tabcellsep Actual (Total)\\
Setosa\tabcellsep 50\tabcellsep 0\tabcellsep 0\tabcellsep 50\\
Versicolor\tabcellsep 0\tabcellsep 47\tabcellsep 3\tabcellsep 50\\
Virginica\tabcellsep 0\tabcellsep 4\tabcellsep 46\tabcellsep 50\\
Predicted (Total)\tabcellsep 50\tabcellsep 51\tabcellsep 49\tabcellsep 150\end{longtable} \par
 
\caption{\label{tab_4}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.33055555555555555\textwidth}P{0.09444444444444444\textwidth}P{0.1259259259259259\textwidth}P{0.11805555555555555\textwidth}P{0.18101851851851852\textwidth}}
\tabcellsep Setosa\tabcellsep Versicolor\tabcellsep Virginica\tabcellsep Actual (Total)\\
Setosa\tabcellsep 50\tabcellsep 0\tabcellsep 0\tabcellsep 50\\
Versicolor\tabcellsep 0\tabcellsep 47\tabcellsep 3\tabcellsep 50\\
Virginica\tabcellsep 0\tabcellsep 4\tabcellsep 46\tabcellsep 50\\
Predicted (Total)\tabcellsep 50\tabcellsep 51\tabcellsep 49\tabcellsep 150\end{longtable} \par
 
\caption{\label{tab_5}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.33055555555555555\textwidth}P{0.09444444444444444\textwidth}P{0.1259259259259259\textwidth}P{0.11805555555555555\textwidth}P{0.18101851851851852\textwidth}}
\tabcellsep Setosa\tabcellsep Versicolor\tabcellsep Virginica\tabcellsep Actual (Total)\\
Setosa\tabcellsep 50\tabcellsep 0\tabcellsep 0\tabcellsep 50\\
Versicolor\tabcellsep 0\tabcellsep 47\tabcellsep 3\tabcellsep 50\\
Virginica\tabcellsep 0\tabcellsep 5\tabcellsep 45\tabcellsep 50\\
Predicted (Total)\tabcellsep 50\tabcellsep 52\tabcellsep 48\tabcellsep 150\end{longtable} \par
 
\caption{\label{tab_6}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.4830935251798561\textwidth}P{0.03669064748201439\textwidth}P{0.09784172661870504\textwidth}P{0.09172661870503597\textwidth}P{0.14064748201438848\textwidth}}
\multicolumn{2}{l}{Setosa}\tabcellsep Versicolor\tabcellsep Virginica\tabcellsep Actual (Total)\\
Setosa\tabcellsep 50\tabcellsep 0\tabcellsep 0\tabcellsep 50\\
Versicolor\tabcellsep 0\tabcellsep 47\tabcellsep 3\tabcellsep 50\\
Virginica\tabcellsep 0\tabcellsep 4\tabcellsep 46\tabcellsep 50\\
Predicted (Total)\tabcellsep 50\tabcellsep 51\tabcellsep 49\tabcellsep 150\\
b) Performance of J48Classifier\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_7}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.15256410256410255\textwidth}P{0.11442307692307692\textwidth}P{0.0953525641025641\textwidth}P{0.09262820512820513\textwidth}P{0.12259615384615383\textwidth}P{0.1389423076923077\textwidth}P{0.13349358974358974\textwidth}}
\tabcellsep Correctly\tabcellsep Incorrectly\tabcellsep \tabcellsep Mean\tabcellsep Root Mean\tabcellsep Time Taken to\\
Test Mode\tabcellsep Classified\tabcellsep Classified\tabcellsep Accuracy\tabcellsep Absolute\tabcellsep Squared\tabcellsep Build Model\\
\tabcellsep Instances\tabcellsep Instances\tabcellsep \tabcellsep Error\tabcellsep Error\tabcellsep (Sec)\\
Split (70\%)\tabcellsep 43\tabcellsep 2\tabcellsep 95.55\%\tabcellsep 0.0416\tabcellsep 0.1682\tabcellsep 0.05\\
5Fold CV\tabcellsep 144\tabcellsep 6\tabcellsep 96\%\tabcellsep 0.035\tabcellsep 0.1582\tabcellsep 0.02\\
10Fold CV\tabcellsep 144\tabcellsep 6\tabcellsep 96\%\tabcellsep 0.035\tabcellsep 0.1586\tabcellsep 0.02\\
15Fold CV\tabcellsep 143\tabcellsep 7\tabcellsep 95.33\%\tabcellsep 0.0395\tabcellsep 0.1758\tabcellsep 0.03\\
20Fold CV\tabcellsep 144\tabcellsep 6\tabcellsep 96\%\tabcellsep 0.0354\tabcellsep 0.1586\tabcellsep 0.03\end{longtable} \par
 
\caption{\label{tab_8}Table 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8} \par 
\begin{longtable}{P{0.34\textwidth}P{0.09714285714285714\textwidth}P{0.12952380952380954\textwidth}P{0.12142857142857141\textwidth}P{0.16190476190476188\textwidth}}
\tabcellsep Setosa\tabcellsep Versicolor\tabcellsep Virginica\tabcellsep Actual (Total)\\
Setosa\tabcellsep 14\tabcellsep 0\tabcellsep 0\tabcellsep 14\\
Versicolor\tabcellsep 0\tabcellsep 16\tabcellsep 0\tabcellsep 16\\
Virginica\tabcellsep 0\tabcellsep 2\tabcellsep 13\tabcellsep 15\\
Predicted (Total)\tabcellsep 14\tabcellsep 18\tabcellsep 13\tabcellsep \end{longtable} \par
 
\caption{\label{tab_9}Table 8 :}\end{figure}
 		 		\backmatter  			 			 			  				\begin{bibitemlist}{1}
\bibitem[Daniel et al. ()]{b0}\label{b0} 	 		‘An Introduction to Data Mining’.  		 			T Daniel 		,  		 			Chantal D Larose 		,  		 			Larose 		.  	 	 		\textit{Computer Science}  		2014.  	 
\bibitem[Danham and Sridhar ()]{b2}\label{b2} 	 		\textit{Data Mining, Introductory and Advanced Topics},  		 			Margaret H Danham 		,  		 			S Sridhar 		.  		2006.  	 	 (Person Education, 1st Edition) 
\bibitem[Mehmed and Kantardzic ()]{b1}\label{b1} 	 		 			M Mehmed 		,  		 			Kantardzic 		.  	 	 		\textit{Data Mining: Concepts, Models, Methods, and Algorithms},  				2002.  	 
\end{bibitemlist}
 			 		 	 
\end{document}
