File Classifier Using Naive Bayes for Enterprise Data
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Solution Overview
Problem
Existing methods for classifying large volumes of electronic data in enterprise environments are either expensive and time-consuming when done manually or inaccurate when relying solely on explicit rules, lacking a balanced and efficient automated solution.
Innovation Solution
A system comprising a file system scanning agent, index, and classifier that compiles and analyzes file attributes and content to categorize files automatically, employing semi-supervised techniques like Naïve Bayes classifiers for improved accuracy and adaptability across various data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification is used, then classification accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The system performs preliminary actions by training the classifier with sample files beforehand. The classifier learns from training data containing files with known classifications, building a knowledge base that enables accurate automated classification without manual intervention during actual classification operations.
Solution Approach 2:
The patent introduces a classifier as an intermediary between manual classification and simple rule-based automation. The classifier acts as a learned mediator that processes files using patterns from training data, achieving accuracy close to manual classification while operating automatically at scale.
2Extent of automation
If explicit rules are used for classification, then automation is improved, but classification accuracy deteriorates
Solution Approach 1:
The system changes the fundamental parameter of classification from static explicit rules to dynamic learned patterns. The classifier adapts its classification behavior based on patterns learned from training data, allowing it to handle non-homogeneous files and complex classification scenarios that rigid rules cannot manage effectively.
Solution Approach 2:
The classification system transitions from static rule-based decision-making to dynamic pattern recognition. The classifier can adapt its classification logic based on the specific characteristics of training data and can handle varying file types and classification requirements through learned behavioral patterns rather than fixed rules.
3Productivity
If more data is classified, then productivity is improved, but manual cost increases
Solution Approach 1:
The classifier enables self-service automated classification without requiring manual intervention for each file. Once trained, the system independently classifies files by comparing them against learned patterns from training data, eliminating the need for human classifiers to manually review and categorize large volumes of files.
Solution Approach 2:
The patent replaces the mechanical system of manual file review and classification with an automated computational classifier. The classifier uses algorithmic processing and pattern recognition to substitute human manual labor, enabling high-volume classification at minimal cost while maintaining accuracy through learned patterns from training data.
Data Source
AI summary
A system for performing data classification operations. In one embodiment, the system comprises a file system configured to store a plurality of computer files and a scanning agent configured to traverse the file system and compile data regarding the attributes and content of the plurality of computer files. The system also comprises an index configured to store the data regarding attributes and content of the plurality of computer files and a file classifier configured to analyze the data regarding the attributes and content of the plurality of computer files and to classify the plurality of computer files into one or more categories based on the data regarding the attributes and content of the plurality of computer files. Results of the file classification operations can be used to set appropriate security permissions on files which include sensitive information or to control the way that a file is backed up or the schedule according to which it is archived.