An improved kNN text classification method Online publication date: Tue, 03-Dec-2019
by Fengfei Wang; Zhen Liu; Chundong Wang
International Journal of Computational Science and Engineering (IJCSE), Vol. 20, No. 3, 2019
Abstract: This paper proposes an improved kNN text classification method. The kNN algorithm in vector space models (VSM) has several limitations, such as occupying excessive storage space and all dimensions in the kNN algorithm share the same weight, making classification inaccurate. To solve these problems, this paper proposes a SOM neural network with principal component weighting. In this model, the principal component analysis process is embedded into the SOM neural network. Specifically, principal component analysis is used to extract the main feature components of the assessed target. Then, it is inputted into the network for computation. Meanwhile, variance contribution rates of principal components are introduced into the Euclidean distance function in the forms of weights. Using the principal component weighting SOM algorithm to compute the weights of VSM dimensions together with the kNN algorithm could effectively reduce dimensions of a vector space, and increase the precision and speed of the kkNN text classification method.
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