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Программный каркас для машинного обучения выбору признаков при классификации сердечных заболеваний с использованием улучшенного алгоритма оптимизации методом роя частиц с классификатором метода опорных векторов
 
Код статьиS013234740003049-8-1
DOI10.31857/S013234740003049-8
Тип публикации Статья
Статус публикации Опубликовано
Авторы
Vijayashree J.  
Аффилиация: School of Computer Science and Engineering, Vellore Institute of Technology
Адрес: Индия, Веллуру
Parveen Sultana H.
Аффилиация: School of Computer Science and Engineering, Vellore Institute of Technology
Адрес: Индия, Веллуру
Название журналаПрограммирование
ВыпускВыпуск 6
Страницы52-64
Аннотация

Машинное обучение используется в качестве эффективной вспомогательной системы диагностики состояния здоровья с большим объемом данных. Чаще всего анализ такого большого объема данных требует больше ресурсов и времени на выполнение. Кроме того, не все признаки, присутствующие в наборе данных, способствуют решению данной задачи. Следовательно, необходимо использовать эффективный алгоритм выбора более важных признаков, которые вносят больший вклад в диагностику заболеваний. Оптимизация методом роя частиц (PSO — Particle Swarm Optimization) — один из метаэвристических алгоритмов для поиска наилучшего решения за меньшее время. В настоящее время алгоритм PSO используется не только для выбора более значимых признаков, но и для устранения ненужных и избыточных, присутствующих в наборе данных. Однако традиционный алгоритм PSO имеет проблему выбора оптимального веса для обновления скорости и положения частиц. Для решения этой проблемы в настоящей работе представлена новая функция определения оптимальных весов на основе функции разнообразия множеств и функции настройки. Мы также предложили новую функцию пригодности для PSO с помощью метода опорных векторов (SVM — Support Vector Machine). Цель функции пригодности — минимизировать количество атрибутов и повысить точность. Эффективность предлагаемого алгоритма PSO-SVM сравнивается с различными существующими алгоритмами выбора признаков, такими как прирост информации, хи-квадрат, алгоритм на основе одного атрибута, подмножество совместимости, алгоритм Relief, метод CFS, отфильтрованное подмножество, отфильтрованный атрибут, нормализованный прирост информации и алгоритм PSO. Классификатор SVM также сравнивается с несколькими классификаторами, а именно с наивным байесовским классификатором, случайным лесом и многослойным перцептроном (MLP — Multilayer perceptron).

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Получено15.12.2018
Дата публикации24.12.2018
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