로지스틱 회귀 분석을 이용한 스펨 필터링의 특징 축소 = Features Reduction using Logistic Regression for Spam Filtering

다국어 초록 (Multilingual Abstract)

Today, The much amount of spam that occupies the mail server and network storage occurs the lack of negative issues, such as overload, and for users to delete the spam should spend time, resources have a problem. Automatic spam filtering on the incidence to solve the problem is essential. A lot of Spam filters have tried to solve the problem emerged as an essential element automatically. Unlike traditional method such as Naive Bayesian, PCA through the many-dimensional data set of spam with a few spindle-dimensional process that narrowed the operation to reduce the burden on certain groups for classification Logistic regression analysis method was used to filter the spam. Through the speed and performance, it was able to get the positive results.

참고문헌 (Reference)

1 H. Drucker, "Support Vector Machines for Spam Categorization" 1999

2 D. Mertz, "Spam Filtering Techniques. Six approaches to eliminating unwanted e-mail" Gnosis Software Inc 1992

3 Toby Segaran, "Programming collective intelligence"

4 M. Vinther, "Junk Detection using neural networks"

5 Pang-Ning Tan, "Introduction to Data Mining"

6 Deerwester, S, "Indexing ByLatent Semantic Analysis" 41 : 391-407, 1990

7 Jiawei Han, "Data mining -Concepts and Techniques" Morgan Kaufmann Publishers 2001

8 Ian H.Witten, "Data Mining: Practical Machine Learning Tools and Techniques"

9 Vikas P. Deshpande, "An Evaluation of Naïve Bayesian Anti-Spam Filtering Techniques" 2007

10 M. Sahami, "A Bayesian Approach to Filtering Junk E-Mail" AAAI 1998