Machine Learning Prediction Model Using Segmented Feature Data Processing
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Solution Overview
Problem
Current machine learning algorithms require a large amount of data and significant operation time to achieve accurate predictions, leading to inefficiencies in data analysis systems.
Innovation Solution
A data analysis system and method that includes a modeling unit for creating prediction models using machine learning algorithms, a feature-extraction unit for classifying feature data into groups, and a processing unit that selects and updates probabilities based on predetermined values to improve prediction efficiency and reduce operation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning algorithm uses a large amount of data and complex processing to achieve accurate prediction, then the prediction accuracy is improved, but the operation time increases
Solution Approach 1:
The patent segments the feature data into multiple groups and processes them in stages rather than all at once. The processing unit selects certain groups for probability calculation based on predetermined conditions, dividing the large-scale data processing into smaller, manageable segments that can be handled more efficiently
Solution Approach 2:
The processing unit performs probability calculations only on selected feature data groups rather than all groups. When the probability meets a predetermined threshold, the system stops processing and outputs results, performing only the necessary partial action required to achieve accurate prediction without unnecessary additional computation
2Reliability
If the machine learning algorithm processes all feature data to ensure accurate prediction, then the prediction reliability is improved, but the processing complexity increases
Solution Approach 1:
The feature data is divided into multiple groups, and the processing unit selectively processes only certain groups based on predetermined conditions. This segmentation approach maintains prediction reliability by ensuring that critical feature groups are processed while avoiding unnecessary processing of other groups, thereby reducing overall processing complexity
Solution Approach 2:
The system performs preliminary classification of feature data into groups before the main probability calculation process. This preliminary action organizes the data structure in advance, allowing the processing unit to efficiently select which groups to process and reducing the complexity of the main processing operation
Data Source
AI summary
A data analysis system includes a modeling unit, a feature-extraction unit, a processing unit and an output unit. The modeling unit creates a prediction model by a machine learning algorithm according to training data. The feature-extraction unit extracts a plurality of fragment of feature data of input data, and classifies the feature data into a plurality of groups. The processing unit obtains a probability of the input data corresponding to the prediction model by the machine learning algorithm according to the feature of one of the groups, and determines the probability. When the probability is less than a predetermined value, the processing unit uses another feature data corresponding to another group which is not used to renew the probability of the input data corresponding to the prediction model through the machine learning algorithm. When the probability is greater than or equal to the predetermined value, the processing unit classifies the input data. The output unit outputs a classification result.