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Related Concept Videos

Ranks01:02

Ranks

444
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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The Availability Heuristic01:08

The Availability Heuristic

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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Related Experiment Video

Updated: Jan 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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Active Feature Acquisition Via Explainability-driven Ranking.

Osman Berke Guney1, Ketan Suhaas Saichandran2,3, Karim Elzokm1

  • 1Department of Electrical & Computer Engineering, Boston University, MA, USA.

Proceedings of Machine Learning Research
|December 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an active feature acquisition (AFA) framework that dynamically selects the most informative features for machine learning models case-by-case. This explainability-driven approach improves predictive accuracy and efficiency in data acquisition.

Related Experiment Videos

Last Updated: Jan 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Acquiring complete data for machine learning is often infeasible due to resource constraints.
  • Static feature selection methods are inadequate when feature importance varies per instance.

Purpose of the Study:

  • To develop an active feature acquisition (AFA) framework for dynamic, instance-specific feature selection.
  • To enhance machine learning model efficiency and predictive accuracy in data-scarce scenarios.

Main Methods:

  • Proposed an active feature acquisition (AFA) framework utilizing local explanation techniques.
  • Reframed AFA as a feature prediction task using a decision transformer-based policy network.
  • Trained the policy network to sequentially acquire features based on instance-specific importance rankings.

Main Results:

  • The proposed AFA method demonstrated superior predictive accuracy compared to state-of-the-art approaches.
  • Achieved higher feature acquisition efficiency, reducing data acquisition costs and time.
  • Validated through extensive experiments on multiple diverse datasets.

Conclusions:

  • An explainability-driven AFA strategy offers a promising solution for efficient feature acquisition.
  • Dynamic, instance-specific feature selection is crucial for optimizing machine learning in practical applications.
  • The developed framework effectively addresses challenges in data acquisition for machine learning models.