Abstract: Systems and methods for classifying URLs by a hybrid classifier are provided. According to one embodiment, a hybrid classifier receives a candidate URL. A naïve Bayes classifier of the hybrid classifier classifies the candidate URL to generate a first classification of the candidate URL and a sublink classifier of the hybrid classifier classifies the candidate URL to generate a second classification of the candidate URL. The hybrid classifier determines a classification of the candidate URL based on the combination of the first and second classification.

Abstract: Systems and methods for classification of web sites and/or their corresponding URLs based on a known web site classification are provided. According to one embodiment, a website URL is received that is known to be associated with a particular content classification. A list of candidate domain names including a host name of the website URL is generated based on a defined TLD list. For each of the candidate domain names it is determined whether an IP address of the candidate domain name is equal to an IP address of the website URL. When the result is affirmative, the particular content classification is associated with the candidate domain name; otherwise, a cosine similarity measurement process is performed between information associated with the candidate domain name and information associated with the website URL to determine whether to associate the particular content classification with the candidate domain name.

Abstract: Systems and methods for classifying URLs by a hybrid classifier are provided. According to one embodiment, a hybrid classifier receives a candidate URL. A naïve Bayes classifier of the hybrid classifier classifies the candidate URL to generate a first classification of the candidate URL and a sublink classifier of the hybrid classifier classifies the candidate URL to generate a second classification of the candidate URL. The hybrid classifier determines a classification of the candidate URL based on the combination of the first and second classification.

Abstract: Systems and methods for improving accuracy of web content classification by removing perceived noise are provided. The system receives a Uniform Resource Locator (URL) of a web page that needs to be classified, and parses the web page so as to construct a tree containing a list of tags. Unwanted tags are removed from the list of tags to yield a tree containing only desired tags that form part of the web page. Subsequently, a list of hyperlinks are based on processing of the tree having desired tags, wherein the list of hyperlinks can include unwanted/undesired/invalid hyperlinks and valid hyperlinks. Unwanted hyperlinks can accordingly be removed from the list of hyperlinks, and each valid hyperlink can be categorized based on a list of categories, and a final category for the web page is determined based on a vector analysis of each category assigned to each valid hyperlink.

Abstract: Systems and methods for classification of web sites and/or their corresponding URLs based on a known web site classification are provided. According to one embodiment, a website URL is received that is known to be associated with a particular content classification. A list of candidate domain names including a host name of the website URL is generated based on a defined TLD list. For each of the candidate domain names it is determined whether an IP address of the candidate domain name is equal to an IP address of the website URL. When the result is affirmative, the particular content classification is associated with the candidate domain name; otherwise, a cosine similarity measurement process is performed between information associated with the candidate domain name and information associated with the website URL to determine whether to associate the particular content classification with the candidate domain name.