Distributed Decision Tree Training via Data Segmentation
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Solution Overview
Problem
Existing methods for training randomized decision trees are inefficient in handling large data sets and require significant computational resources, particularly when distributed across multiple processing nodes, as they often rely on sequential processing and inadequate data distribution strategies.
Innovation Solution
A distributed processing system that splits training data into subsets and distributes them across multiple slave processing units, using a breadth-first approach to iteratively expand the decision tree, with each slave unit identifying optimal split functions and updating indices to improve processing efficiency and reduce memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sequential processing methods are used for training randomized decision trees, then implementation simplicity is maintained, but training speed and productivity are significantly reduced
Solution Approach 1:
The training data is divided into multiple partitions that can be processed independently and in parallel across different processing nodes. Each partition is assigned to a specific worker node, enabling simultaneous processing of multiple data subsets without requiring complex coordination between nodes during the training process.
Solution Approach 2:
The patent transitions from sequential single-node processing to distributed multi-node parallel processing by adding the dimension of spatial distribution across multiple processing nodes. This dimensional change enables simultaneous execution of training operations on different data partitions, dramatically improving training speed while maintaining manageable system complexity through standardized node interfaces.
2Reliability
If large data sets are processed using traditional methods, then comprehensive training is achieved, but computational resource requirements and processing time increase significantly
Solution Approach 1:
Large training datasets are segmented into smaller partitions that can be distributed across multiple processing nodes. This segmentation enables parallel processing of comprehensive data without requiring any single node to handle the entire dataset, reducing processing time while maintaining the benefit of training on complete data through aggregation of results from all partitions.
Solution Approach 2:
Data partitions are preprocessed and prepared in advance, with necessary computations performed locally on each partition before aggregation. This preliminary action on distributed data subsets reduces the computational burden during the main training phase, enabling faster processing of large datasets while preserving training quality.
3Productivity
If distributed processing is implemented without optimized data distribution, then parallel processing capability is utilized, but communication overhead and system complexity increase
Solution Approach 1:
The patent divides training data into distinct partitions that map directly to individual worker nodes, creating a simple one-to-one correspondence between data segments and processing units. This segmentation strategy eliminates the need for complex data shuffling or redistribution during training operations, reducing communication overhead while maintaining efficient parallel processing.
Solution Approach 2:
Each worker node processes its assigned data partition independently with local computations optimized for that specific subset. This local processing approach minimizes inter-node communication requirements, as each node operates autonomously on its designated data portion, thereby reducing system complexity while preserving parallel processing benefits.
Data Source
AI summary
A method training a randomized decision tree through multiple iterations, each is based on:a) Receiving multiple data samples that include data subsets, each data subset corresponds to an attribute.b) Distributing the data subsets to slave processing units after sorting the data samples in consecutive ascending order by updating a first index that identifies trajectories of the training data samples through the tree nodes of the previous tree level.c) Simultaneously processing the data subsets to identify split functions for each tree node with respect to each data subset and updating a second index that identifies the trajectories of the training data samples through the tree node of the current tree level.d) Collecting the split functions from the slave processing units and constructing the current tree level by selecting a preferred split function for each tree node of the current tree level.