Abstract:
Today, the amount of Amharic digital documents has grown rapidly. Because of this, automatic text classification is extremely
important. Proper selection of features has a crucial role in the accuracy of classification and computational time. When the initial
feature set is considerably larger, it is important to pick the right features. In this paper, we present a hybrid feature selection
method, called IGCHIDF, which consists of information gain (IG), chi-square (CHI), and document frequency (DF) features’
selection methods. We evaluate the proposed feature selection method on two datasets: dataset 1 containing 9 news categories and
dataset 2 containing 13 news categories. Our experimental results showed that the proposed method performs better than other
methods on both datasets 1and 2. *e IGCHIDF method’s classification accuracy is up to 3.96% higher than the IG method, up to
11.16% higher than CHI, and 7.3% higher than DF on dataset 2, respectively.
