Predictable Field Identification for Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Choosing the best machine learning technique for a specific application is challenging due to the availability of generic and inefficient options, which are not domain-specific, leading to dependency on experts and limited functionality across different data types and fields.
Innovation Solution
A method and system that receive inputs from various sources, cluster them using domain knowledge, identify patterns based on correlation and predictability factors, optimize the data, and recommend machine learning techniques for predictable fields within the application, ensuring relevance and efficiency based on a predictability score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic machine learning techniques are used across different applications, then the availability of ML options is high, but the efficiency and relevance of ML analytics deteriorates
Solution Approach 1:
The system applies local quality by customizing machine learning techniques according to specific application domains and data characteristics. Instead of using generic ML approaches uniformly, the system identifies predictable fields within each application context and selects specialized ML techniques tailored to those specific domains, thereby improving efficiency while maintaining versatility through context-aware adaptation.
Solution Approach 2:
The system changes parameters by dynamically selecting ML techniques based on identified predictable fields and domain-specific characteristics. The approach transforms the static selection of generic ML methods into a dynamic process where technique selection adapts to the specific parameters of each application's data structure, domain knowledge, and predictability metrics.
2Measurement precision
If experts manually analyze application data to recommend ML techniques, then the accuracy of recommendations is high, but the dependency on experts and complexity increases
Solution Approach 1:
The system implements self-service by enabling automatic identification of predictable fields and recommendation of machine learning techniques without requiring expert intervention. The system autonomously analyzes application data, clusters inputs using domain knowledge, identifies patterns in predictable fields, and selects appropriate ML techniques based on predefined criteria and optimization algorithms.
Solution Approach 2:
The system substitutes the mechanical process of expert manual analysis with an automated computational system. Instead of relying on human experts to manually evaluate data and recommend ML techniques, the system uses automated clustering, pattern identification, and optimization algorithms to perform the same function, thereby maintaining accuracy while eliminating dependency on human experts.
3Productivity
If domain-specific ML techniques are developed for each application, then the efficiency of ML analytics is improved, but the complexity of selecting and implementing techniques increases
Solution Approach 1:
The system applies segmentation by dividing the complex task of ML technique selection into manageable components: identifying predictable fields, clustering inputs based on domain knowledge, analyzing patterns in specific fields, and selecting techniques for each identified predictable field. This segmented approach breaks down the overall complexity into discrete, automated steps that can be executed systematically.
Solution Approach 2:
The system performs preliminary action by pre-processing and clustering application data using domain knowledge before ML technique selection. The system identifies predictable fields and establishes patterns in advance, creating a structured foundation that simplifies subsequent technique selection and implementation, thereby reducing the complexity of the overall process.
4Reliability
If comprehensive data analysis is performed before ML recommendation, then the relevance of ML techniques is improved, but the time and resources required increase
Solution Approach 1:
The system extracts only the essential and relevant features from application data that are necessary for ML technique recommendation. Instead of performing exhaustive analysis of all data aspects, the system focuses on identifying predictable fields and their patterns, extracting only the critical information needed for effective ML technique selection, thereby reducing analysis time while maintaining relevance.
Data Source
AI summary
This disclosure relates generally to identifying predictable fields in an application for machine learning (ML). With the availability of several choices for machine learning techniques, it is difficult to choose the most effective option on a specific application. In addition, the functionality/usage of fields within an application may vary across applications subject to the application's domain. Hence ML may not be efficient for all datatypes/fields. Therefore, the disclosure provides a method and system for identifying predictable fields in an application before ML technique for the predictable fields. The predictable fields are identified based on the domain of the application using a grouping technique, a pattern identification technique and optimization techniques. Further ML techniques are recommended only on identified predictable fields, thereby making the ML process more effective on the application in relevance with the application's domain.