Aditya polumetla in partial fulfillment of the requirements for the degree of master of science
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SITE: Airport code where the AWOS unit is located LON: Longitude LAT: Latitude Appendix B Using WEKA WEKA is written in Java and is organized into packages arranged in a hierarchical manner. Details of the packages and the hierarchy are given by Witten & Frank [2005]. WEKA can be run using its graphical user interface or through entering textual commands in the command prompt. The general structure of the WEKA textual command, to perform multiple 10-fold cross-validations on a dataset using an algorithm (classifier) is java -mx1024M -cp classpath callClassifier classifier_path classifier_options -t trainset.arff -x 10 -s seed_value -c attribute_index where -cp specifies the path (i.e., the class path) where WEKA is located, callClassifier3 is a java class that is used to output the complete class probability without which WEKA outputs an evaluative result of the algorithm, classifier_path is the location of the algorithm in the WEKA package hierarchy, classifier_options specifies the options taken by an algorithm, -t specifies the training file, -x specifies the number of folds for cross-validation, -s is used to indicated the seed value when a multiple n-fold cross-validations need to be preformed, -c specifies the output attributes position in the dataset provided. The -T option is used when a test file is used for evaluating the model, when not used a cross validation is preformed on the training set provided. WEKA requires the data in the train/test file to be in ARFF format. The general format of an ARFF file is given in Table B1. The string @relation is used to mention the name of the dataset, @attribute is used to define the attributes name and type and @data is used to
indicate the start of the data, which is in a comma-separated form. Following are the classifier_path for the machine learning algorithms that were used in this thesis along with their default options (classifier_options)
weka.classifiers.functions.LinearRegression -S 0 -R 1.0E-8 where -S specifies the attribute selection methods with 0 representing the M5 method, and -R specifies the value of the ridge parameter.
weka.classifiers.functions.LeastMedSq –S 4 –G 0 where -S specifies the size of random samples used to generate the least squared regression function, and -G specifies the seed value used to select subsets of the training data.
weka.classifiers.trees.M5P –M 4.0 where -M specifies the minimum number of instances.
weka.classifiers.functions.MultilayerPerceptron -L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a where -L specifies the learning rate, -M specifies the momentum, -N specifies the number of training epochs, -V specifies validation set size, -S specifies the seed value taken by the random number generator (random values are used for initialization of weights), -E specifies the validation threshold, and -H specifies the number of hidden layers with its value 'a' representing (num_attributes+num_classes)/2 layers.
weka.classifiers.functions.RBFNetwork -B 2 -S 1 -R 1.0E-8 -M -1 -W 0.1 where -B specifies the number of clusters generated by K-means, -S specifies the value of the seed passed on to the K-means, -R specifies the value of the ridge parameter, -M specifies the number of iterations to be performed by logistic regression, and -W specifies the minimum standard deviation for the clusters.
weka.classifiers.rules.ConjunctiveRule -N 3 -M 2.0 -P -1 -S 1 where -N specifies the amount of data used for pruning, -M specifies the minimum total weight of the instances in a rule, -P specifies the minimum number of antecedents allowed in a rule when pre-pruning is used, and -S specifies the seed value used. J48 weka.classifiers.trees.J48 -C 0.25 -M 2 where -C specifies the confidence factor, and -M specifies the minimum number of instances taken by a leaf
weka.classifiers.bayes.NaiveBayes Bayes Net weka.classifiers.bayes.BayesNet -D -Q weka.classifiers.bayes.net.search.local.K2 -- -P 1 -E weka.classifiers.bayes.net.estimate.SimpleEstimator -- -A 0.5 -D is used to prevent memory problems with ADTree is used, -Q specifies the search algorithm, and -E specifies the estimator used for finding the CPTs. K2 search algorithm is given by weka.classifiers.bayes.net.search.local.K2 with its option -P specifying the maximum number of parents taken by a node in the Bayesian network. The estimator used for filling up the CPTs is weka.classifiers.bayes.net.estimate.SimpleEstimator, with its option -A specifying the alpha value of the estimator. Appendix C Detailed Results Table C.1: Results obtained from using regression algorithms to predict temperature at an RWIS site (Experiment 1). Feature vector consists of temperature information from RWIS-AWIS sites in a set along with temperature offset for the RWIS sites. The table has mean absolute error values averaged over ten 10-fold cross-validations.
StdDev refers to Standard Deviation  Figure C.1: Mean absolute errors for different RWIS sites obtained from predicting temperature using regression algorithms.
Table C.2: Results obtained from using regression algorithms to predict temperature at an RWIS site (Experiment 2). Feature vector consists of temperature information from RWIS-AWIS sites in a set along with precipitation type for the RWIS sites. The table has mean absolute error values averaged over ten 10-fold cross-validations.
StdDev refers to standard deviation  Figure C.2: Mean absolute errors for different RWIS sites obtained from predicting temperature using regression algorithms, with precipitation type information added to the feature vector.
Table C.3: Results obtained from using classification algorithms to predict precipitation type at an RWIS site (Experiment 4). Feature vector consists of temperature information from RWIS-AWIS sites in a set along with precipitation type for the RWIS sites. The table has classification error values, as reported by WEKA, averaged over ten 10-fold cross-validations.
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