این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Artificial Intelligence and Data Mining، جلد ۱۲، شماره ۳، صفحات ۴۰۹-۴۲۱
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی A New Structure for Perceptron in Categorical Data Classification
چکیده انگلیسی مقاله Artificial neural networks are among the most significant models in machine learning that use numeric inputs. This study presents a new single-layer perceptron model based on categorical inputs. In the proposed model, every quality value in the training dataset receives a trainable weight. Input data is classified by determining the weight vector that corresponds to the categorical values in it. To evaluate the performance of the proposed algorithm, we have used 10 datasets. We have compared the performance of the proposed method to that of other machine learning models, including neural networks, support vector machines, naïve Bayes classifiers, and random forests. According to the results, the proposed model resulted in a 36% reduction in memory usage when compared to baseline models across all datasets. Moreover, it demonstrated a training speed enhancement of 54.5% for datasets that contained more than 1000 samples. The accuracy of the proposed model is also comparable to other machine learning models.
کلیدواژه‌های انگلیسی مقاله Neural network,qualitative data,categorical data,non-numeric data,binary classification
نویسندگان مقاله Fariba Taghinezhad |
Department of Electrical and Computer Engineering, Yazd University, Yazd, Iran.

Mohammad Ghasemzadeh |
Department of Electrical and Computer Engineering, Yazd University, Yazd, Iran.

نشانی اینترنتی https://jad.shahroodut.ac.ir/article_3340_56b09593c0c37e4e58ad4acaf9f52f45.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات