Hybrid Feature Selection for Amharic News Document Classification

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. The 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.

(Retracted) News image text classification algorithm with bidirectional encoder representations from transformers model

“…3 Endalie's research proposed a news image text feature classification method based on gain and chi-square mixture, which can improve the speed of classification. 4 Firdaus 5 improved the classification accuracy of news image text through Bayesian algorithm. Classification algorithms, such as machine learning, can improve the efficiency of classifying news images and texts, but it still takes a lot of time to build image and text feature models.…”

“…Tegegnie’s research pointed out that the automatic classification of news images and texts depends on the classification of relevant features 3 . Endalie’s research proposed a news image text feature classification method based on gain and chi-square mixture, which can improve the speed of classification 4 . Firdaus 5 improved the classification accuracy of news image text through Bayesian algorithm.…”

Automated Amharic News Categorization Using Deep Learning Models

“…Text classification is well studied in many languages and, in particular, the English language [ 3 ]. Despite the fact that various works have been done to develop an Amharic document classifier, they all rely on machine learning models [ 4 – 6 ]. One of the most difficult problems in machine learning is reducing the dimensionality of a high-dimensional feature space.…”