Text Classifier Selecting Machine Learning Algorithms by Goal Type
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Solution Overview
Problem
Selecting the most suitable machine learning algorithms for text classification tasks is challenging due to the need for deep domain expertise and understanding of the algorithms' performance in specific contexts, which is often not available.
Innovation Solution
A computer program and method that uses natural language processing to classify text based on features and relationships, determining a goal type, and then selects machine learning algorithms with performance measurements most correlated to the goal type, optimizing the selection of algorithms for improved predictive outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple machine learning algorithms are used in ensemble learning, then predictive accuracy is improved, but algorithm selection complexity increases due to the need for deep domain expertise
Solution Approach 1:
The patent introduces a classifier as an intermediary component that automatically determines the goal type of input text. This classifier mediates between the raw input and the ensemble of machine learning algorithms, selecting which algorithms to apply based on the classified goal type. This eliminates the need for users to have deep domain expertise in algorithm selection while maintaining high predictive accuracy through appropriate algorithm matching.
Solution Approach 2:
The system enables self-service by allowing the input text itself to determine which algorithms should be applied. The text's features and goal type automatically guide the selection process without requiring external expert intervention. The ensemble learning system serves itself by using the input characteristics to configure its own algorithm composition.
2Measurement precision
If domain expertise is required to understand algorithm performance in specific contexts, then algorithm selection accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by using the classifier to automatically determine goal types and select appropriate algorithms without requiring user expertise. The text's inherent features drive the selection process, making the system easy to operate while maintaining accurate algorithm matching through the classifier's goal type determination.
3Ease of operation
If machine learning algorithms are selected without understanding their performance measurements, then ease of operation is improved, but predictive accuracy deteriorates
Solution Approach 1:
The classifier acts as an intermediary that bridges the gap between ease of operation and predictive accuracy. It automatically analyzes input text features and goal types to select algorithms with appropriate performance characteristics, eliminating the need for users to understand performance measurements while ensuring accurate algorithm selection for high predictive accuracy.
Data Source
AI summary
Provided are a computer program product, system, and method for classifying text to determine a goal type used to select machine learning algorithm outcomes. Natural language processing of text is performed to determine features in the text and their relationships. A classifier classifies the text based on the relationships and features to determine a goal type. The determined features and relationships from the text are inputted into a plurality of different machine learning algorithms to generate outcomes. For each of the machine learning algorithms, a determination is made of performance measurements resulting from the machine learning algorithms generating the outcomes. A determination is made of at least one machine learning algorithm having performance measurements that are highly correlated to the determined goal type. An outcome is determined from at least one of the outcomes.