A New Multi-class Classification Method Based on Machine Learning to Document Classification.

In the field of classification, input vectors are labeled with one of several predetermined classes. Automated systems scour a wide range of industries—from e-commerce to news outlets to blogs to directories to content curators—in search of applicable applications. To make suggestions about what to read and how to find it, natural language processing (NLP) uses document classification. Due to the difficulties that big datasets with many categories can provide for conventional document categorization approaches, this research uses machine learning for multi-class document classification. The suggested approach utilizes feature extraction and machine learning approaches to solve these issues. Features such as word frequency (TF) and term frequency-inverse document frequency (TF-IDF) are retrieved from documents to represent textual content, enabling the model to classify features based on word semantics. The method utilizes the chi-square test and the Bat Algorithm to choose features optimally. Random Forest (RF), Stochastic Gradient Descent (SGD), Adaptive Boosting (AdaBoost), and Cat Boost are only a few of the cutting-edge machine-learning algorithms used in the multi-class classification approach. These algorithms, which are trained to take advantage of the retrieved features, perform well when presented with multi-class document categorization challenges. Extensive testing on various datasets has validated the efficacy of the suggested methodology, demonstrating its improved accuracy and processing efficiency compared to traditional methods. Among the various available options, the Random Forest (RF) algorithm has proven to be the most effective, with an accuracy rate of 87.57%. This method’s flexibility makes it an invaluable tool in the NLP field since it may be successfully used in various document classification tasks.