The present inventions generally relate to a leaf node ranking method in decision trees for spatial prediction and its recording medium. The leaf node ranking method in decision trees includes a learning step to form a decision tree having one root node, in which each parent node has multiple child nodes, using training data sets for spatial prediction; and a leaf node ranking step from the decision tree that finishes the learning. In the learning step, each node of the decision tree stores both the number of classes according to class distribution of training data and structures for storing the number. In the leaf node ranking step, a rank of a leaf node is determined using the number of classes according to class distribution, which is stored in each node on a path from the root node to the leaf node.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A leaf node ranking method in decision trees, performed by a computer, for spatial prediction, comprising: a spatial prediction data preparation operation that performs a geometric correction to a spatial event occurrence layer and a spatial event inducing environment layer in order that locations of the layers correspond to locations of each other with the same range, the layers being prediction targets and being data that the decision tree learns; a decision tree construction operation that constructs a decision tree with one root node, in which each parent node has multiple child nodes, using the spatial prediction data as training data; a leaf node ranking operation that reflects a ranking result calculated from a root node on a child node using the decision tree, and calculates a rank using the number of event-occurring classes and of non-event-occurring classes, the number of classes being included in each node on paths from the root node to all leaf nodes; and a prediction result obtaining operation that maps the rank (priority) of the leaf node to a location on a spatial coordinate, each node storing the number of event-occurring classes and of non-event-occurring classes in which training data is classified, for computing the rank of the leaf node. 2. The method of claim 1, wherein the decision tree with one root node, in which each parent node has multiple child nodes, is comprised of a child node R(node.child) and a parent node R(node.parent), and the child node R(node.child) and the parent node R(node.parent) are defined as equation 4, R(node.parent)≦R(node.child) [equation 4] wherein, when a rank of the parent node (R(node.parent)) is the same as a rank of the child node (R(node.child)), the child node does not include a spatial event-occurring class, and when the rank of the child node is more than the rank of the parent node, the child node includes an event-occurring class. 3. The method of claim 1, wherein the leaf node ranking operation is performed using equation 5. R  ( node . child ) = R  ( node . parent ) + n event ( n event + n non_event ) × d , [ equation   5 ] and where d represents a depth of a node in a tree. 4. A computer-readable storage medium storing a leaf node ranking method in decision trees performed by a computer, for spatial prediction according to claim 1.