Vector-Space Data Classification for Unknown Object Types
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Solution Overview
Problem
Existing data classification systems face inefficiencies in classifying data objects of unknown type due to the arbitrary arrangement of classifiers, leading to high computational overhead and time consumption, especially in large datasets with varied data types, and are hindered by the challenges of constructing effective blocking components.
Innovation Solution
A method that represents data objects as vectors in a multidimensional space, clusters data domain vectors, sorts clusters by distance to the object vector, and iteratively applies classifiers within sorted clusters, providing a confidence metric for successful classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If classifiers are arranged in arbitrary order, then the system is simple to implement, but the classification process requires approximately N/2 attempts on average, resulting in high computational overhead and time consumption
Solution Approach 1:
The patent applies preliminary action by pre-processing the classification data to identify and prioritize classification rules before the actual classification process. The system analyzes the dataset to determine which classification rules are most likely to match the data objects, and arranges these rules in an optimized order beforehand. This preliminary arrangement reduces the average number of attempts needed during classification from N/2 to significantly fewer attempts, thereby improving classification efficiency without complicating the implementation.
2Measurement precision
If more classifiers are configured to cover all possible data domains, then classification accuracy improves, but the number of attempts and computational overhead increase significantly
Solution Approach 1:
The patent applies segmentation by dividing the large set of classification rules into smaller, manageable groups or subsets. Instead of applying all N classifiers to every data object, the system segments the rules based on data characteristics, data types, or other relevant criteria. This allows the system to apply only the relevant subset of classifiers to each data object, maintaining high classification accuracy while significantly reducing the time and computational overhead required.
Solution Approach 2:
The patent applies partial action by determining that full classification using all N classifiers is excessive for many data objects. The system uses preliminary analysis to identify which classifiers are necessary for each specific data object, applying only the required portion of the classification rules. This partial approach maintains accuracy for the specific data at hand while avoiding the time loss associated with applying all classifiers universally.
3Ease of operation
If classifiers are applied in a fixed arbitrary order, then the system is easy to manage, but it cannot adapt to the distribution of data objects, leading to suboptimal classification performance
Solution Approach 1:
The patent applies dynamics by making the classifier arrangement adaptive rather than static. The system dynamically adjusts the order and selection of classification rules based on the actual distribution and characteristics of the data objects being classified. This dynamic approach allows the system to optimize its performance for different datasets and scenarios while maintaining ease of management through automated adaptation, eliminating the need for manual reconfiguration.
Data Source
AI summary
An apparatus, computer-readable medium, and computer-implemented method for efficiently classifying a data object, including representing the data object as a data object vector in a vector space, each dimension of the data object vector corresponding to a different feature of the data object, determining a distance between the data object vector and centroids of data domain clusters in the vector space, each data domain cluster comprising data domain vectors representing data domains, sorting the data domain clusters according to their respective distances to the data object vector, and iteratively applying data domain classifiers corresponding to data domains represented in a closest data domain cluster in the sorted data domain clusters to the data object.