Ordered Sequence Data Identification in ML Datasets
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Solution Overview
Problem
Machine learning models often require large datasets with ordered sequences to function effectively, but random sampling and shuffling of data suppress the original sequence, making it difficult to identify and utilize ordered sequence data for accurate predictions.
Innovation Solution
A system and method that utilize a machine learning algorithm to predict the contents of subsequent frames in a dataset by partitioning the data into frames, applying a machine learning algorithm to determine ordered sequences, and comparing prediction accuracy with threshold values to identify and store ordered sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If random sampling and shuffling is applied to datasets, then machine learning models can be trained with diverse data samples, but the original sequence information is suppressed and lost
Solution Approach 1:
The dataset is divided into multiple frames, where each frame contains a subset of data samples. This segmentation allows the system to process and analyze sequence information within each frame while maintaining the ability to shuffle across frames, thus preserving sequence information locally while achieving global data diversity for training.
2Measurement precision
If ordered sequence data is preserved in datasets, then prediction accuracy can be improved, but data processing complexity increases
Solution Approach 1:
The system extracts and identifies ordered sequence data from the dataset by analyzing patterns within frames. Machine learning algorithms are applied to detect sequence information, and only the relevant ordered sequences are extracted and preserved for training, reducing the complexity of processing entire datasets while maintaining prediction accuracy.
3Measurement precision
If machine learning algorithms are applied to identify ordered sequences, then prediction accuracy improves, but computational time and resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing the dataset into frames and pre-identifying potential ordered sequences before actual training. This preliminary organization and identification reduce the computational burden during the main training phase, as the data is already structured and sequence information is pre-marked, thus reducing overall computational time while maintaining accuracy.
Data Source
AI summary
A system includes one or more processors configured to execute the instructions to perform a method for determining the ordered sequence. In the method, a dataset is retrieved from a database. The dataset comprises a data matrix comprising a plurality of elements or cells arranged in a set of rows and columns. The dataset is partitioned into a plurality of frames comprising a first subset of the set of rows and columns, the plurality of frames being in a sequential order. A machine learning algorithm to the dataset to predict contents of a next frame in the sequential order. Comparing the predicted contents of the next frame with actual contents of the next frame to determine a prediction accuracy value, and if the prediction accuracy value of the predicted contents exceeds a first threshold level, storing the predicted contents of the next frame.