Weighted Search Tree Document Classification via Genetic Algorithms
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Solution Overview
Problem
Conventional document classification methods, such as Boolean logic, naive Bayes' classifier, decision trees, and support vector machines, are often complex and lack specificity, necessitating a simpler yet effective classification approach.
Innovation Solution
A weighted search tree is employed, utilizing leaf nodes with assigned terms and locations, and common nodes with operators and weights, optimized by genetic algorithms to determine document relevance scores, allowing for classification based on term occurrences and operator calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional classification methods (Boolean logic, naive Bayes, decision trees, SVMs) are used, then classification accuracy can be achieved, but the system complexity and difficulty of operation increase
Solution Approach 1:
The patent segments the classification problem into a hierarchical tree structure with internal nodes representing logical operators and leaf nodes representing term conditions. This segmentation breaks down complex classification logic into manageable, visually representable segments that are easier to construct and modify while maintaining classification accuracy.
Solution Approach 2:
The patent transitions from traditional flat classification approaches to a two-dimensional representation combining visual tree structures with mathematical scoring. The tree structure provides visual dimension for logic organization, while the relevance score dimension provides quantitative measurement, together offering a more intuitive and controllable classification system.
2Adaptability or versatility
If conventional classification methods are used, then classification can be performed, but the specificity and flexibility of search logic decrease
Solution Approach 1:
The patent implements dynamic classification through weighted relevance scoring that can adapt to different document types and classification needs. The weights assigned to different tree nodes can be adjusted dynamically to reflect changing classification priorities, allowing the system to maintain high specificity while being flexible in its approach.
Solution Approach 2:
The patent uses parameter changes in the form of relevance scores and weights that can be modified to adjust classification sensitivity and specificity. By changing these parameters, the system can optimize for different levels of precision and recall without restructuring the entire classification logic, thereby maintaining both flexibility and precision.
3Ease of operation
If simple classification approaches are used, then ease of operation improves, but classification effectiveness and reliability decrease
Solution Approach 1:
The patent introduces visual tree structures as an intermediary between the user and the classification algorithm. This visual representation serves as a mediator that makes complex classification logic accessible and easy to construct, while the underlying mathematical scoring system ensures reliable and effective classification results.
Solution Approach 2:
The patent replaces traditional mechanical classification systems (Boolean logic gates, decision tree traversals) with a unified relevance score calculation mechanism. This substitution simplifies the operational complexity by providing a single, consistent scoring approach while maintaining the logical rigor needed for reliable classification.
Data Source
AI summary
A device for classifying a document comprises a module to generate a data tree structure and configured to assign terms to a first plurality of nodes of the data tree structure, where each of the first plurality of nodes is assigned a weight. In assigning the weights of the first plurality of nodes, a first generation of combinations of possible weights assignable as the weights of the first plurality of nodes is obtained, and a second generation of combinations of possible weights assignable as the weights of the first plurality of nodes is obtained by performing the genetic algorithms in the first generation of combinations of possible weights. The device determines whether the document is in a document class based at least the weights of the first plurality of nodes.