Provide an overview of economic systems. Discuss their most essential features. Also, reflect on their advantages and disadvantages.
Sample Solution
publications mentioned above conclude that making use of PNR data improves forecasting performance. The PNR data mining approach models cancellation rate forecasting as a two-class probability estimation problem (Morales & Wang, Forecasting Cancellation Rates for Services Booking Revenue Management Using Data Mining, 2009). Popular two-class probability estimation methods are tree-based methods and kernel-based methods. Probability estimation trees estimate the probability of class membership, in our case the probability that a booking will be cancelled or not. Quinlan (1993) developed an algorithm, C4.5, that generates decision trees. The trees produced by C4.5 are small and accurate, resulting in fast reliable classifiers and therefore decision trees are valuable and popular methods for classification. In contrast to Provost and Domingos (2003) who concluded that the performance of conventional decision-tree learning programs is poor and therefore they have made some modifications to the C4.5 algorithm. The C4.4 uses information gain criteria to divide the tree nodes and no pruning is used. Fierens, Ramon, Blockeel and Bruynooghe (2005) concluded that overall the C4.4-approach outperforms the C4.5-approach. However, the trees of the C4.5-approach are much smaller than for the C4.4- approach. The C4.4 method builds a single tree, however, random forests can improve the predictive performance of a single tree by aggregating many decision trees. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forests (Breiman, 2001). For a large number of trees, it follows from the Strong Law of Large Numbers and the tree structure that random forests always converge so that overfitting is not a problem (Breiman, 2001). In random forests, the idea is to decorrelate the several trees and then reduce the variance in the trees by averaging them (Random Forests in R, 2017). Averaging the trees helps to reduce the variance and improve the performance of the trees and eventually avoid overfitting. Kernel based methods make use of kernel functions which map input data points to a higher dimensional space, such that a linear method in a new space becomes non-linear in the original space and therefore these methods are able to model non-linear relationships between dependent and independent variables (Morales & Wang, Forecasting Cancellation Rates for Services Booking Revenue Management Using Data Mining, 2009). One of the most popular kernel based methods for class probability estimation is Support Vector Machine (SVM). If we have labelled data, SVM can be used to generate multiple separating hyperplanes such that the data space is divided into segments and each segment contains only one kind of data (Machine Learning Using Support Vector Machines, 2017). SVM is able to find the hyperplane that creates the biggest margin between the training points f>
publications mentioned above conclude that making use of PNR data improves forecasting performance. The PNR data mining approach models cancellation rate forecasting as a two-class probability estimation problem (Morales & Wang, Forecasting Cancellation Rates for Services Booking Revenue Management Using Data Mining, 2009). Popular two-class probability estimation methods are tree-based methods and kernel-based methods. Probability estimation trees estimate the probability of class membership, in our case the probability that a booking will be cancelled or not. Quinlan (1993) developed an algorithm, C4.5, that generates decision trees. The trees produced by C4.5 are small and accurate, resulting in fast reliable classifiers and therefore decision trees are valuable and popular methods for classification. In contrast to Provost and Domingos (2003) who concluded that the performance of conventional decision-tree learning programs is poor and therefore they have made some modifications to the C4.5 algorithm. The C4.4 uses information gain criteria to divide the tree nodes and no pruning is used. Fierens, Ramon, Blockeel and Bruynooghe (2005) concluded that overall the C4.4-approach outperforms the C4.5-approach. However, the trees of the C4.5-approach are much smaller than for the C4.4- approach. The C4.4 method builds a single tree, however, random forests can improve the predictive performance of a single tree by aggregating many decision trees. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forests (Breiman, 2001). For a large number of trees, it follows from the Strong Law of Large Numbers and the tree structure that random forests always converge so that overfitting is not a problem (Breiman, 2001). In random forests, the idea is to decorrelate the several trees and then reduce the variance in the trees by averaging them (Random Forests in R, 2017). Averaging the trees helps to reduce the variance and improve the performance of the trees and eventually avoid overfitting. Kernel based methods make use of kernel functions which map input data points to a higher dimensional space, such that a linear method in a new space becomes non-linear in the original space and therefore these methods are able to model non-linear relationships between dependent and independent variables (Morales & Wang, Forecasting Cancellation Rates for Services Booking Revenue Management Using Data Mining, 2009). One of the most popular kernel based methods for class probability estimation is Support Vector Machine (SVM). If we have labelled data, SVM can be used to generate multiple separating hyperplanes such that the data space is divided into segments and each segment contains only one kind of data (Machine Learning Using Support Vector Machines, 2017). SVM is able to find the hyperplane that creates the biggest margin between the training points f>
