BOOSTED DECISION TREES FOR EVALUATING FEATURE VECTORS

摘要

The disclosure is directed to evaluating feature vectors using decision trees. Typically, the number of feature vectors and the number of decision trees are very high, which prevents loading them into a processor cache. The feature vectors are evaluated by processing the feature vectors across a disjoint subset of trees repeatedly. After loading the feature vectors into the cache, they are evaluated across a first subset of trees, then across a second subset of trees and so on. If the values based on the first and second subsets satisfy a specified criterion, further evaluation of the feature vectors across the remaining of the decision trees is terminated, thereby minimizing the number of trees evaluated and therefore, consumption of computing resources.

主权项

1. A method performed by a computing system, comprising:
receiving multiple feature vectors, wherein at least some of the feature vectors include a plurality of features; receiving multiple decision trees using which the feature vectors are to be evaluated; loading the feature vectors into a memory of the computing system; and loading disjoint subsets of the decision trees into the memory of the computing system successively for evaluating the feature vectors, the loading the disjoint subsets successively including:
loading a first subset of the disjoint subsets into the memory,evaluating the feature vectors using the first subset to generate a first result,evicting the first subset from the memory, andloading a second subset of the disjoint subsets into the memory after evicting the first subset.