The k-nearest Neighbor Classification of Histogram- and Trapezoid-Valued Data
| العنوان: | The k-nearest Neighbor Classification of Histogram- and Trapezoid-Valued Data |
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| المؤلفون: | Mostafa Razmkhah, Fathimah Al-Ma'shumah, Sohrab Effati |
| المصدر: | Statistics, Optimization & Information Computing. 10:1187-1203 |
| بيانات النشر: | International Academic Press, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Statistics and Probability, Control and Optimization, Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Statistics, Probability and Uncertainty, Information Systems |
| الوصف: | A histogram-valued observation is a specific type of symbolic objects that represents its value by a list of bins (intervals) along with their corresponding relative frequencies or probabilities. In the literature, the raw data in bins of all histogram-valued data have been assumed to be uniformly distributed. A new representation of such observations is proposed in this paper by assuming that the raw data in each bin are linearly distributed, which are called trapezoid-valued data. Moreover, new definitions of union and intersection between trapezoid-valued observations are made. This study proposes the k-nearest neighbor technique for classifying histogram-valued data using various dissimilarity measures. Further, the limiting behavior of the computational complexities based on the performed dissimilarity measures are compared. Some simulations are done to study the performance of the proposed procedures. Also, the results are applied to three various real data sets. Eventually, some conclusions are stated. |
| تدمد: | 2310-5070 2311-004X |
| DOI: | 10.19139/soic-2310-5070-1451 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::b5ad02a0a0fe249cd25f9eac57bb6df5 https://doi.org/10.19139/soic-2310-5070-1451 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi...........b5ad02a0a0fe249cd25f9eac57bb6df5 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23105070 2311004X |
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| DOI: | 10.19139/soic-2310-5070-1451 |