Constrained Decision Tree Induction for Causal Logic
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing decision trees in data mining often produce unsatisfactory models due to illogical splitter order, difficulty in understanding and interpreting causal factors, and conflict with scientific beliefs or business logic, as they do not adequately control which attributes are used as splitters or in what order.
Innovation Solution
The method imposes constraints on the induction of decision trees to control which attributes can be used as splitters, specifying conditions such as region, sample size, cost-complexity, and attribute characteristics, allowing for structured tree growth that aligns with business or causal logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If decision trees are built using recursive partitioning without constraints, then the tree can be grown quickly and automatically, but the splitter order becomes illogical and conflicts with business logic or causal beliefs
Solution Approach 1:
The patent applies preliminary action by pre-defining constraints on attribute usage before the tree construction process begins. These constraints specify which attributes can be used as splitters in which regions of the tree, ensuring that business logic and causal relationships are embedded in advance. This allows the tree to be built automatically while maintaining logical consistency with domain knowledge.
Solution Approach 2:
The patent changes the parameters of the tree construction process by introducing constraint parameters that limit which attributes can be used as splitters in specific regions. This modifies the standard recursive partitioning algorithm to operate within defined boundaries, ensuring that splitters are selected not just based on statistical criteria but also according to pre-specified business rules and causal relationships.
2Ease of operation
If constraints are imposed on attribute usage in decision tree regions, then interpretability and alignment with business logic improve, but the complexity of specifying and managing constraints increases
Solution Approach 1:
The patent applies segmentation by dividing the decision tree into distinct regions and assigning different constraint sets to each region. This allows constraints to be managed in a modular fashion, where each region has its own rules about which attributes can be used as splitters. This segmentation reduces the overall complexity by breaking down the constraint specification task into smaller, more manageable pieces.
Solution Approach 2:
The patent introduces an intermediary layer between the data and the tree construction process - the constraint specification system. This intermediary translates business logic and causal relationships into formal constraints that guide the tree building algorithm. By acting as a mediator, it simplifies the interface between domain knowledge and the technical construction process.
3Measurement precision
If exhaustive search is performed over all possible attribute splits, then the most statistically optimal split is found, but the computational time and resources increase significantly
Solution Approach 1:
The patent changes the search space parameters by limiting which attributes can be considered as splitters in each region based on pre-defined constraints. Instead of performing exhaustive search over all attributes, the algorithm only searches over the subset of attributes that are permitted by the constraints for that particular region. This maintains statistical optimality within the constrained space while significantly reducing computational time.
Data Source
AI summary
The present invention provides a method and system for imposing constraints on the induction of decision trees to control where in the decision tree, and in what order specific attributes may be used as node splitters. The invention is used to force a decision tree to contain a specified attribute in one or more locations, or to require the construction of a decision tree in which nodes split using one group of attributes always appear above or always appear below nodes using another group of attributes. This constrained tree is grown and optionally pruned in the same way that a prior art tree is grown and optionally pruned, subject only to the constraints imposed by the method of the present invention.