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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Multi-input and Multi-variable systems

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Updated: Apr 22, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Segment Based Decision Tree Induction With Continuous Valued Attributes.

Ran Wang, Sam Kwong, Xi-Zhao Wang

    IEEE Transactions on Cybernetics
    |October 8, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for decision tree (DT) induction using continuous attributes. By considering example segments alongside frequency, it improves accuracy and reduces tree size.

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    Last Updated: Apr 22, 2026

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    7.0K

    Area of Science:

    • Machine Learning
    • Data Mining
    • Artificial Intelligence

    Background:

    • Decision tree (DT) induction with continuous attributes faces challenges in node splitting.
    • Traditional methods using candidate cut points (CCPs) based on frequency may overlook class permutations and fail to differentiate similar cut points, potentially leading to larger, less effective trees.

    Purpose of the Study:

    • To propose a novel strategy for node splitting in decision trees with continuous attributes.
    • To enhance the effectiveness and reduce the size of decision trees by addressing limitations of frequency-based heuristic measures.

    Main Methods:

    • Introduced a new concept of 'segment of examples' to differentiate candidate cut points (CCPs) with similar frequency information.
    • Developed a hybrid scheme combining frequency and segment-based measures for splitting decision tree nodes.
    • Analyzed the relationship between frequency and the expected number of segments as a random variable.

    Main Results:

    • The proposed hybrid scheme effectively differentiates candidate cut points (CCPs) that traditional frequency-based methods cannot.
    • Experimental results show significant improvements in the generalization capability of decision trees.
    • The new approach successfully reduces the overall size of the induced decision trees.

    Conclusions:

    • The novel segment-based approach offers a more nuanced strategy for decision tree node splitting.
    • This method enhances decision tree performance by improving generalization and reducing model complexity.
    • The findings provide a valuable advancement for decision tree induction with continuous attributes.