String Kernel Model for Inheritance Data Classification
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Solution Overview
Problem
Current data classification methods fail to accurately identify related records in large databases due to the complexity of comparing vast amounts of data, particularly in inheritance datasets where recombination points are not considered.
Innovation Solution
A method using a string kernel model to generate feature vectors from matched data strings between a target inheritance dataset and reference datasets, combined with a decision tree model for classification, to predict the classification of named entities while considering recombination points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current data classification methods are used to identify related records in large databases, then the process can be performed with simple algorithms, but the accuracy of identifying related records deteriorates due to inability to consider recombination points and continuity of matched data strings
Solution Approach 1:
The patent segments the inheritance dataset into continuous matched data strings rather than treating individual data points independently. By identifying and analyzing continuous segments that span recombination points, the method captures structural information about inheritance patterns, thereby improving the accuracy of identifying related records while maintaining computational feasibility through focused segment analysis.
Solution Approach 2:
The patent introduces a new dimension of analysis by considering the continuity and length of matched data strings across recombination points. Instead of merely counting matched single data points, the method evaluates the spatial extent and continuity of matches along the inheritance dataset, adding a dimensional aspect that captures recombination structure and improves classification accuracy.
2Measurement precision
If a string kernel model is applied to matched data strings considering recombination points, then the measurement precision of data similarity improves, but the computational complexity increases
Solution Approach 1:
The patent extracts and focuses computational resources on continuous matched data strings that are relevant to inheritance relationships, rather than processing all possible data point comparisons. By identifying and isolating these continuous segments, the method reduces the effective computational space while maintaining high measurement precision through targeted string kernel analysis on the extracted relevant portions.
Solution Approach 2:
The patent changes the parameter of analysis from individual data point matching to continuous string matching with specific attention to recombination points. By transforming the input representation to emphasize continuous segments and their properties (length, continuity, position relative to recombination points), the string kernel model achieves higher precision with more efficient computation on the transformed parameter space.
3Reliability
If recombination points and continuity of matched data strings are considered, then the reliability of community predictions improves, but the ease of operation deteriorates due to increased complexity in data processing
Solution Approach 1:
The patent performs preliminary identification and marking of recombination points and continuous matched data strings before the main classification process. By pre-processing the inheritance dataset to highlight these critical features, the subsequent community prediction process becomes more reliable as it operates on pre-organized information, while the ease of operation is partially restored through the structured preparation phase that simplifies later analysis.
Data Source
AI summary
Disclosed is a method for predicting classification of named entities. The method may include receiving a target inheritance dataset of a target named entity and a plurality of reference inheritance datasets corresponding to a plurality of reference named entities. The method may include generating a feature vector corresponding to the target inheritance dataset by applying a string kernel model to matched data strings between the target inheritance dataset and each of the reference inheritance datasets and generating the feature vector based on results of applying the string kernel model to the matched data strings between the target inheritance dataset and the plurality of reference inheritance datasets. The method may include applying a decision tree model to the feature vector corresponding to the target inheritance dataset and generating an output using the decision tree model. The output may provide information associated with a data classification of the target named entity.