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Register a new userConical Classification For Efficient One-Class Topic Determination
التصنيف المخروطي لتصميم موضوع فيء من فئة واحدة
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كما ينمو الإنترنت في الحجم، فهذا يفعل مقدار المعلومات القائمة على النص الموجود.بالنسبة للعديد من المساحات التطبيق، فإن الأمر أساسي لعزل وتحديد النصوص التي تتعلق بموضوع معين.في حين أن التصنيف من الفئة من الفئة سيكون مثاليا لهذه التحليل، فهناك نقص قريب في البحث فيما يتعلق بالنهج الفعالة مع قوة تنبؤية عالية.من خلال الإشارة إلى أن مجموعة المستندات التي يرغبنا في تحديدها كمجموعات خطية إيجابية لنموذج مساحة المتجهات التي تمثل نصنا، نقترح تصنيف مخروطي، وهو نهج يسمح لنا بتحديد ما إذا كان المستند من موضوع معين في حسابيبطريقة فعالة.نقترح أيضا استبعاد طبيعي، نسخة معدلة من الفصل العادي الذي يجعله أكثر ملاءمة في سياق التصنيف من فئتين.نظهر في تحليلنا أن نهجنا ليس لديه فقط قوة تنبؤية فقط على مجموعات البيانات الخاصة بنا، ولكنه أسرع أيضا في حسابه.
As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient approaches with high predictive power. By noting that the range of documents we wish to identify can be represented as positive linear combinations of the Vector Space Model representing our text, we propose Conical classification, an approach that allows us to identify if a document is of a particular topic in a computationally efficient manner. We also propose Normal Exclusion, a modified version of Bi-Normal Separation that makes it more suitable within the one-class classification context. We show in our analysis that our approach not only has higher predictive power on our datasets, but is also faster to compute.
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