USES THE BAGGING ALGORITHM OF CLASSIFICATION METHOD WITH WEKA TOOL FOR PREDICTION TECHNIQUE

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1 USES THE BAGGING ALGORITHM OF CLASSIFICATION METHOD WITH WEKA TOOL FOR PREDICTION TECHNIQUE 1 POOJA SHRIVASTAVA, 2 MANOJ SHUKLA 1 Computer Science and Engineering, Jayoti Vidyapeeth Women s University, of Ph.D. Research Scholar (JVR-II/12/5010) Jaipur, India 2 Computer Science and Engineering, Associate Professor, Sunder deep group of Institution, Ghaziabad, U.P. Abstract- Classification is the more important concept of data mining and it is a form of data analysis. Classifiers increasing the accuracy in data mining techniques. This paper presents the analysis of accuracy for forest fire database in UCI machine learning with the help of classification techniques. This analysis work is performed with bagging algorithm and WEKA tool. Keywords- Bagging Algorithm, Classification Concept, Forest Fire Data Set, WEKA Tool. I. INTRODUCTION Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.classification consists of assigning a class label to a set of unclassified cases.classification is a form of data analysis such analysis can help provide us with a better understanding of the data at large. we introduce the concept of datamining classification concept. Forest fire is one type of significant disturbance to the forest ecosystem. Many researcher is analysis more accuracy in this data sets. But it is very challenging task for researcher. Some researcher introduced the prediction of forest fire in many way. We presented the accuracy for forest fire data set. Data classification is two-steps process, consisting of a learning steps. and a classification step. We uses the WEKA tool in this research.because The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to this functionality. this WEKA Tool version 3_6_9. Data mining technique is one of the common Section approaches to determine the accuracy. In this paper. we explore the bagging algorithm. Bagging works as a method of increasing accuracy. Bagging is based on the concepts of bootstrapping and aggregation. The bootstrap method samples the given training tuples uniformly with replacement. Bagging can be applied to the prediction of continuous values by taking the average value of each prediction for a given test tuple. Data mining research & tools have focused on commercial area applications. Only a fewer data mining research have focused on scientific data and it s so difficult and it; very hard but many researcher analyzing these data properly. II- OVERVIEW OF UCI MACHINE LEARNING AND FOREST FIRE DATA SET Machine learning investigates the mechanisms by which knowledge is acquired through experience. Research at UCI spans the spectrum of models for learning, including those based on statistics, logic, mathematics, neural structures, information theory, and heuristic search algorithms.our research involves the development and analysis of algorithms that identify patterns in observed data in order to make predictions about unseen data. New learning algorithms often result from research into the effect of problem properties on the accuracy and run-time of existing algorithms. We investigate learning from structured databases (for applications such as screening loan applicants), image data (for applications such as character recognition), and text collections (for applications such as locating relevant sites on the World Wide Web). UCI also maintains the international machine learning database repository, an archive of over 100 databases used specifically for evaluating machine learning algorithm. In [Cortez and Morais, 2007], the output 'area' was first transformed with a ln (x+1) function. Then, several Data Mining methods were applied. After fitting the models, the outputs were post-processed with the inverse of the (x+1) transform. Four different input setups were used. The experiments were conducted using a 10- fold (cross-validation) x 30 runs. Two regression metrics were measured: MAD and RMSE. A Gaussian support vector machine (SVM) fed with only 4 direct weather conditions (temp, RH, wind and rain) obtained the best MAD value: (mean and confidence interval within 95% using a t- student distribution). The best RMSE was attained by the naive mean predictor. An analysis to the regression error curve (REC) shows that the SVM 23

2 model predicts more examples within a lower admitted error. In effect, the SVM model predicts better small fires, which are the majority. Table 1-Attribute Description X x-axis coordinate (from 1 to 9) Y y-axis coordinate (from 1 to 9) month Month of the year (January to December) day Day of the week (Monday to Sunday) Input =D, a set of d training tuples; =k; the no model in the ensemble; = a classification learning schema (REP tree) Output : the ensemble a composite model M*. An ensemble tends to be more accurate than its base classifiers. III. IMPLEMENTATION AND RESULT- FFMC FFMC code DMC DMC code DC DC code ISI ISI index temp Outside temperature (in C) RH Outside relative humidity (in %) wind Outside wind speed (in km/h) rain Outside rain (in mm/m2) area Total burned area (in ha) in this table The first four rows denote the spatial and temporal attributes. month and day of the week temporal variables. Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI). The first three are related to fuel codes: the FFMC offer to the moisture content surface litter and influences ignition and fire spread, while the DMC and DC denotes the moisture content of shallow and deep organic layers, which affect fire intensity. The ISI is a score that correlates with fire velocity spread. temp, RH,wind rain is the metrological variables. A.Introduction of Bagging Algorithm- Bootstrap aggregating, also called bagging, is a machine learning ensemble metaalgorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach. Bagging works as a method of increasing accuracy. So we use these algorithm in WEKA tool for forest fire data set. The bagged classifier often has significantly greater accuracy than a single classifier derived from D, the original training data. Bagging are example of ensemble methods this method improve composite classification model. Bagging algorithms uses with REP tree in this research.rep tree is part of the decision tree induction. A DT is a flow chart like tree structure. In WEKA tool bagging algorithm is present the with REP tree. Create an ensemble of classification models for a learning schema where each model gives an equally weighted prediction. In this research given a set D, of d tuples. Bagging algorithm allows on the forest fire data set. We used WEKA tool because it offers a variety of learning algorithms. And we implement bagging algorithm in this tool because it is advance method for determine the accuracy. In forest fire dataset Number of Instances is 517 and no of attributes is 13. Data set is denoted by D. For iteration (i=1,2...k) a training set Di of d tuples is sampled with replacement from the original set of tuples. Bagging stands for bootstrap aggregation. Method: 1-for i=1 to k do // create k models: 2-create bootstrap sample,di by sampling D with replacement: 3-use Di and the learning scheme to derive a model, Mi; 4-end for To use the ensemble to classify a tuple, X; Let each of the k models classify X and return the majority vote; We apply the bagging algorithm on the data set D and classifier output builds the model of dataset D. In these the size of tree is 60, 83 and 59. In this research learning scheme to drive a model Mi and after that we chose return the majority vote. These model shows that the size of forest fire data set in UCI machine learning using with the WEKA tool. Apply the algorithm in WEKA tool explorer then the classifier output is shown in below. Table 2-Classifier model (full training set) of size of tree is 60 day = mon month = jan : (0/0) [0/0] month = feb : 6.64 (2/0) [1/99.2] month = mar : 6.56 (4/1.77) [3/424.16] month = apr : 3.35 (0/0) [1/96.5] month = may : (0/0) [0/0] month = jun : 2.89 (3/0.55) [2/0.69] month = jul : (4/ ) [1/ ] month = aug : 0.54 (6/0) [7/2.37] month = sep temp < DMC < : 0.76 (15/4.05) [6/0.95] DMC >= : 4.98 (3/3.2) [1/1.27] temp >= : (2/0) [0/0] month = oct 24

3 FFMC < 88.3 : 5.44 (2/0) [1/0] FFMC >= 88.3 : (2/0) [2/0] month = nov : (0/0) [0/0] month = dec X-axis < 3.5 : (2/0) [0/0] X-axis >= 3.5 : (2/0) [3/11.65] day = tue wind < 2.45 Y-axis < 3 : (3/0) [1/0] Y-axis >= 3 : (5/2.38) [2/10.52] wind >= 2.45 ISI < : 8.15 (32/51.79) [13/ ] ISI >= : 36.6 (2/229.52) [1/229.52] day = wed : (27/ ) [14/800.15] day = thu X-axis < 7.5 : 1.93 (35/5.32) [18/66.98] X-axis >= 7.5 wind < 4.7 wind < 2.9 : (3/42.2) [4/258.15] wind >= 2.9 : 0.64 (4/1.27) [2/0.94] wind >= 4.7 : (2/0) [2/ ] day = fri month = jan : 4.58 (0/0) [0/0] month = feb DMC < 5.25 : 4.62 (2/0) [0/0] DMC >= 5.25 : 0 (3/0) [1/0] month = mar : 0.96 (7/8.46) [7/1.52] month = apr : 0 (1/0) [0/0] month = may : (1/0) [0/0] month = jun : 1.19 (1/0) [1/0] month = jul : 1.32 (2/1.74) [0/0] month = aug RH < 35 : (3/183.55) [2/229.44] RH >= 35 ISI < DMC < 154 : 1.72 (4/2.64) [2/1.93] DMC >= 154 : 0.12 (7/0.04) [0/0] ISI >= : 8.76 (3/2.47) [0/0] month = sep : 5.22 (33/78.91) [14/117.6] month = oct : 0 (1/0) [1/0] month = nov : 4.58 (0/0) [0/0] month = dec : 9.27 (1/0) [0/0] day = sat : 12.9 (61/400.88) [29/ ] day = sun : 9.53 (54/172.26) [31/ ] In above table we can see that first classifier model is build which size of tree is 60. These model shows that how attributes of forest fire work on the WEKA tool and provide the accuracy of data set. Table 2.1-Classifier model (full training set) of size of tree is 83 DMC < Y-axis < 7 X-axis < 8.5 DMC < 128 month = jan : 0 (2/0) [1/0] month = feb : 4.61 (17/36.58) [1/79.9] month = mar : 4.06 (40/104.03) [13/22.26] month = apr : (4/3.56) [3/ ] month = may : 0 (1/0) [0/0] month = jun : 1.93 (9/9.47) [3/3.58] month = jul : 1.41 (3/0) [10/11.91] month = aug temp < : 0.92 (32/1.3) [18/7.63] temp >= : 6.42 (6/10.63) [4/14.4] month = sep wind < 4.7 temp < DC < X-axis < 7.5 X-axis < 2.5 RH < 45.5 : 7.76 (4/1.44) [4/12.51] RH >= 45.5 : 1.06 (3/0.6) [0/0] X-axis >= 2.5 : 0.92 (35/1.83) [17/23.31] X-axis >= 7.5 day = mon : 0 (1/0) [0/0] day = tue : 5.72 (0/0) [0/0] day = wed : 5.72 (0/0) [0/0] day = thu : 5.72 (0/0) [0/0] day = fri : (1/0) [0/0] day = sat : 1.95 (2/0) [1/0] day = sun : 2.01 (2/0) [0/0] DC >= : (2/0) [0/0] temp >= : (5/478.99) [7/ ] wind >= 4.7 : 7.53 (16/161.25) [14/135.06] month = oct day = mon : 6.14 (2/0.48) [0/0] day = tue : 1.12 (0/0) [0/0] day = wed : 0 (0/0) [1/2.35] day = thu : 1.12 (0/0) [0/0] day = fri : 0 (1/0) [1/0] day = sat : 0 (0/0) [1/2.35] day = sun : 0 (5/0) [0/0] month = nov : 7.91 (0/0) [0/0] month = dec day = mon : (3/14.51) [1/7.25] day = tue : (2/0) [1/0] day = wed : (1/0) [1/0] day = thu : (0/0) [0/0] day = fri : 9.27 (2/0) [0/0] day = sat : (0/0) [0/0] day = sun : (0/0) [0/0] DMC >= 128 day = mon : 0.96 (9/1.13) [3/0.5] day = tue X-axis < 5.5 temp < : 2.13 (13/13.16) [7/6.34] temp >= : (5/0.71) [0/0] X-axis >= 5.5 : (8/ ) [3/884.73] day = wed : (15/524.03) [13/700.79] day = thu : 2.2 (10/17.71) [6/14.73] day = fri 25

4 month = jan : 6.27 (0/0) [0/0] month = feb : 6.27 (0/0) [0/0] month = mar : 6.27 (0/0) [0/0] month = apr : 6.27 (0/0) [0/0] month = may : 6.27 (0/0) [0/0] month = jun : 6.27 (0/0) [0/0] month = jul : (1/0) [0/0] month = aug DC < : (11/120.76) [2/0.7] DC >= : 0.3 (8/0.49) [2/0.14] month = sep : 0 (4/0) [0/0] month = oct : 6.27 (0/0) [0/0] month = nov : 6.27 (0/0) [0/0] month = dec : 6.27 (0/0) [0/0] day = sat : 0.54 (2/0.65) [1/0.65] day = sun X-axis < 7.5 : 11.3 (20/423.68) [18/ ] X-axis >= 7.5 : (2/9651.1) [1/9651.1] X-axis >= 8.5 : (4/ ) [0/0] Y-axis >= 7 : (2/ ) [2/ ] DMC >= : (29/ ) [13/ ] Table 2.2-Classifier model (full training set) of size of tree is 59 RH < 25.5 day = mon : (0/0) [0/0] day = tue : (0/0) [0/0] day = wed : (0/0) [2/ ] day = thu : 2.44 (3/0) [2/61.79] day = fri : 0 (1/0) [0/0] day = sat : (2/0) [0/0] day = sun : (0/0) [0/0] RH >= 25.5 temp < : 6 (85/105) [45/294.67] temp >= temp < 23.1 : (4/10.52) [1/31.56] temp >= 23.1 day = mon : 5.88 (0/0) [0/0] day = tue : 0.96 (1/0) [1/0] day = wed : 0 (1/0) [1/0] day = thu : (1/0) [1/0] day = fri : 0 (1/0) [0/0] day = sat : 0 (4/0) [1/0] day = sun X-axis < 4.5 : 5.21 (4/0) [1/0] X-axis >= 4.5 : (2/0) [1/0] temp >= : (3/214.66) [1/ ] month = oct DMC < 47.5 : 0 (6/0) [5/0] DMC >= 47.5 : 6.83 (2/0) [1/0] month = nov : 8.94 (0/0) [0/0] month = dec : (4/42.25) [2/14.51] month = jan : 0 (1/0) [0/0] month = feb wind < 5.85 : 3.49 (14/0.3) [7/410.08] wind >= 5.85 : (3/5.55) [1/309.17] month = mar : 3.25 (28/9.69) [16/162.62] month = apr : 3.49 (10/18.66) [3/26.48] month = may : (2/0) [1/0] month = jun day = mon : 0 (0/0) [1/104.36] day = tue : 6.61 (0/0) [0/0] day = wed : 0 (2/0) [1/0] day = thu : 6.61 (0/0) [0/0] day = fri : 0.6 (2/0.35) [2/0.35] day = sat : (1/0) [0/0] day = sun : 0 (2/0) [0/0] month = jul : (17/ ) [14/582.42] month = aug ISI < : 4.96 (124/318.73) [58/585.31] ISI >= : (5/375.54) [0/0] month = sep temp < Y-axis < 2.5 day = mon : (0/0) [0/0] day = tue : (1/0) [0/0] day = wed : (0/0) [0/0] day = thu : 0 (3/0) [2/0] day = fri : 0 (4/0) [1/0] day = sat : (1/0) [1/0] day = sun : (0/0) [0/0] Y-axis >= 2.5 In above tables we can see that builds models, which counts as one vote and these one vote to find the best accuracy. Time taken to build model 0.13 seconds. These model shows that the prediction the size of forest fires. Table 2.3 Cross-validation Summary Correlation coefficient Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 517 In this shows that cross validation summary in this experiment cross validation folds is 10.It s the summary of cross validation and shows the errors of build the model. These errors include the data set D. Now we taken training data set in WEKA tool experiment we performed in testing with paired T- tester corrected tester. In this experiment significance is 0.05 and comparison field is below in table. In this table we shows that test output of forest fires dataset. Table 2.4 testing the experiment of forest fires data set Comparison field Test output X-axis 4.67 Y-axis 4.30 FFMC

5 DMC DC ISI 9.02 Temp RH wind 4.02 rain 0.02 area This table show that the test output of data set D. These output define the size of forest fire data set and these experiment improve the accuracy and prediction technique. In this research we can predict the problem of forest fires easily. Data mining is very vast area. Some researcher can not solve problem easily in scientific data. But with the help of data mining we can improve knowledge discovery. A. Statistic value of forest fires data- In this section we draw statistic value of data set and draw the area graph. This is explorer application in WEKA tool. Table 2.5 Statistic value of forest fire data set S.No. Statistic value Attributes name Minimum Maximum Mean Std Dev X-axis Y-axis FFMC DMC DC ISI Temp RH wind rain area Above these table we can see that minimum and maximum value of attributes and mean and Std Dev define the statistic value of forest fire data set. CONCLUSION Data mining is very large and vast area in this world. Now days data mining use in every sectors. It s algorithm so easy for implementation in tool. The main objective in this paper explain the classification method and observers the perfect accuracy for forest fires data set in UCI machine learning. This paper focus on analyzing the dataset which given that UCI machine learning.this algorithm build a classifier model of data set then predict the whose model is accurate. These model shows that size of forest fire. WEKA tool is the machine learning and we use this tool in this research. REFERENCES [1] Panˇce Panov and Saˇso Dˇzeroski Department of Knowledge Technologies Joˇzef Stefan Institute. [2] Bernard _Zenko, Ljup_co Todorovski, and Sa_so D_zeroski Department of Intelligent Systems, Jo_zef Stefan Institute Jamova 39, Ljubljana, Slovenia. [3] Yong Poh Yu, Rosli Omar1, Rhett D. Harrison, Mohan Kumar Sammathuria and Abdul Rahim Nik[Journal of Computational Biology and Bioinformatics Research Vol. 3(4), pp , July 2011 Available online ISSN Academic Journals]. [4] Paulo Cortez1 and An ıbal Morais1 Department of Information Systems/R&D Algoritmi Centre, University of Minho, Guimar aes, Portugal. [5] K.Muralidharan II Year B.Tech Information Technology Karpagam Institute of Technology COIMBATORE 21. [6] Book name- Data mining concept and techniques [jiawei han, Micheline Kamber, Jain Pei]. 27