Cải thiện phân loại email thông qua phương pháp tiền xử lý dữ liệu nâng cao

Spatial Information Research - Tập 29 - Trang 247-255 - 2021
B. Aruna Kumara1, Mallikarjun M. Kodabagi1, Tanupriya Choudhury2, Jung-Sup Um3
1CSE Department, REVA University, Bangalore, India
2Deparment of Informatics, School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, India
3Department of Geography, College of Social Sciences, Kyungpook National University, Daegu, South Korea

Tóm tắt

Email đã trở thành một trong những hình thức giao tiếp được sử dụng rộng rãi nhất, dẫn đến sự gia tăng theo cấp số nhân trong số lượng email nhận được và tạo ra một gánh nặng khổng lồ cho các phương pháp phân loại email hiện có. Việc áp dụng phương pháp phân loại trên dữ liệu thô có thể làm giảm hiệu suất của các thuật toán phân loại. Do đó, dữ liệu cần được chuẩn bị để nâng cao hiệu suất của các bộ phân loại học máy. Bài báo này đề xuất một phương pháp tiền xử lý dữ liệu nâng cao cho phân loại email đa danh mục. Mô hình đề xuất sẽ loại bỏ chữ ký trong email. Hơn nữa, các ký tự đặc biệt và từ ngữ không mong muốn sẽ được loại bỏ bằng các phương pháp tiền xử lý khác nhau như loại bỏ từ dừng, loại bỏ từ dừng nâng cao và stemming. Mô hình đề xuất được đánh giá bằng cách sử dụng nhiều bộ phân loại như Multi-Nominal Naïve Bayes, Bộ Phân loại Vector Hỗ trợ Tuyến tính, Hồi quy Logistic và Rừng ngẫu nhiên. Các kết quả cho thấy phương pháp tiền xử lý dữ liệu được đề xuất cho phân loại email vượt trội hơn so với phương pháp hiện có.

Từ khóa

#phân loại email #tiền xử lý dữ liệu #học máy #Multi-Nominal Naïve Bayes #Rừng ngẫu nhiên

Tài liệu tham khảo

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Spatial Information Research

Tập 29

247-255

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