Random forest technology-based similar file retrieval method

Abstract

The invention relates to a random forest technology-based similar file retrieval method. Compared with the prior art, the method has the advantage that the defect of incapability of performing effective retrieval in a specific field is overcome. The method comprises the following steps of organizing a judgment document; constructing a case characteristic tree; performing weight training on the case characteristic tree, performing training for different targets by adopting a random forest method, and calculating a comprehensive weight of case characteristics; obtaining retrieval information, and inputting filtration conditions and query conditions of the retrieval information in an input mode of conditional selection, a text with conditions or the whole judgment document; calculating a case similarity matrix; and outputting a retrieval result, obtaining similar cases from the case similarity matrix, finding n cases most similar to the query conditions or cases with the similarity greater than s, making statistics on the information, and performing visual display. According to the method, the problems of instability and over-fitting caused by a single decision-making tree are effectively avoided by using the random forest-based method.

Description

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AI Technical Summary

Benefits of technology

This patented process helps search or match related documents more accurately without being affected from any issues like stability and overfitness. It uses Random Forest (RF) techniques that use specific technical factors such as industrial properties and expertise. These methods improve upon existing algorithms but still have limitations due to their fixed structure.

Problems solved by technology

The technical problem addressed in this patents relates to improving the efficiency and effectiveness of searching documents related to specific areas like legal case searches or investigative procedures conducted during litanyings. Current methods either require manual input or lack precision due to factors like subjectivity involved while navigating through irrelevant data sources. This can lead to poorly identified scenarios that may result in incorrect conclusions being drawn up later when reviewing them again afterward. There exists a challenge where current systems only provide basic level recommendations without providing detailed analysis capabilities.

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