A K-Nearest-Neighbour Based Classifier for Securities Text Categorization

Tie Bin Liu

doi:10.4028/www.scientific.net/AMR.989-994.1541

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994A K-Nearest-Neighbour Based Classifier for...

A K-Nearest-Neighbour Based Classifier for Securities Text Categorization

Abstract:

Event-driven investments have gained great importance and popularity. Due to the importance of the timely and effective messages for successful investment, the automated categorization of documents into predefined labels has received an ever-increased attention in the recent years. This paper implements a new text document classifier by integrating the K-nearest neighbour (KNN) classification approach with the VSM vector space model. By screening the feature items and weighted key items, the proposed classifier turns the financial information text into N-dimensional vector and identified the positive and negative information, furthermore achieve to the classification optimized. In addition, the classification model constructed by the proposed algorithm can be updated incrementally, and it has great scalability in event-driven securities investment for investors.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

1541-1546

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.1541

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Online since:

July 2014

Authors:

Tie Bin Liu*

Keywords:

Event Driven, Information Securities Investment, KNN, Text Categorization, VSM

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* - Corresponding Author

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