Measuring variability and association for categorical data

Allaj, Erindi

doi:10.1016/j.fss.2020.11.018

Cited by 2 publications

(3 citation statements)

References 22 publications

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“…Equation ( 9) represents the normalized matrix obtained using Energy distance, defined in (8), for the Trento dataset.…”

Section: A Trentomentioning

confidence: 99%

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On Measures of Uncertainty in Classification

Chlaily,

Ratha,

Lozou

et al. 2023

IEEE Trans. Signal Process.

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Uncertainty is unavoidable in classification tasks and might originate from data (e.g., due to noise or wrong labeling), or the model (e.g., due to erroneous assumptions, etc). Providing an assessment of uncertainty associated with each outcome is of paramount importance in assessing the reliability of classification algorithms, especially on unseen data. In this work, we propose two measures of uncertainty in classification. One of the measures is developed from a geometrical perspective and quantifies a classifier's distance from a random guess. In contrast, the second proposed uncertainty measure is homophily-based since it takes into account the similarity between the classes. Accordingly, it reflects the type of mistaken classes. The proposed measures are not aggregated, i.e., they provide an uncertainty assessment to each data point. Moreover, they do not require label information. Using several datasets, we demonstrate the proposed measures' differences and merit in assessing uncertainty in classification. The source code is available at github.com/pioui/uncertainty.

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“…Equation ( 9) represents the normalized matrix obtained using Energy distance, defined in (8), for the Trento dataset.…”

Section: A Trentomentioning

confidence: 99%

“…Different attempts exist in the literature for computing the variance of categorical data. Nonetheless, the general practice is substituting probabilities by relative frequencies in the Gini-Simpson index or Shannon entropy [8].…”

Section: Introductionmentioning

confidence: 99%

On Measures of Uncertainty in Classification

Chlaily,

Ratha,

Lozou

et al. 2023

IEEE Trans. Signal Process.

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“…Data description techniques vary depending on the data type. Quantitative data types in variables utilize measures of frequency [21], central tendency [22], dispersion [23], and kurtosis and skewness [24]. Qualitative data types in variables, on the other hand, employ the technique of calculating the frequency of occurrence and the proportion or percentage of each value category [22].…”

Section: Figure 1 Stages Of Studymentioning

confidence: 99%

Evaluating the Influence of the International Civil Aviation Organization on Aircraft Accident Rates and Fatalities: A Seven-Decade Historical Data Analysis

Passarella,

Veny,

Fachrurrozi

et al. 2023

ATAMS

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The advent of air travel, originally proposed by the Wright brothers, has led to a significant surge in aircraft usage for human transportation. In its nascent stages, this mode of transport was linked with a high frequency of accidents and consequent fatalities, placing it in the high-risk category. To counter these risks, the International Civil Aviation Organization (ICAO) was established in 1947 as a collaborative effort among numerous countries with the primary goal of enhancing aviation safety regulations. This study analyzed archival data from the Bureau of Aircraft Accidents Archives (B3A), covering a span of 72 years from 1918, the year of the first commercial airplane crash, until 2020. The objective was to understand the ICAO's impact on altering accident rates, fatalities, and underlying causes. Analytical methodologies encompassed both descriptive statistics-examining data distribution, central tendencies, and category frequencies-and exploratory data analysis (EDA) to identify variable relationships and outlier identification. The results indicated that ICAO's interventions have led to a notable decline in accident rates, with an annual average reduction of 70.9%, and a corresponding decrease in incidents attributed to technical factors. However, an unexpected trend was the increase in fatalities despite the drop in accident numbers, attributable to the introduction of larger aircraft designs carrying more passengers per flight. The findings underscore the ICAO's successful efforts in reducing aircraft accidents, but also suggest a need for further exploration into factors contributing to the rise in fatalities.

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Measuring variability and association for categorical data

Cited by 2 publications

References 22 publications

On Measures of Uncertainty in Classification

On Measures of Uncertainty in Classification

Evaluating the Influence of the International Civil Aviation Organization on Aircraft Accident Rates and Fatalities: A Seven-Decade Historical Data Analysis

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