Exploration and implementation of classification algorithms for patent classification

Abstract

Data mining techniques have seen tremendous increase in their usage in the past few years. Patent mining is one of the domains that utilize data mining techniques to a great extent. Patent mining consists of various tasks such as retrieval of patent, classification, patent valuation, patent visualization and detecting infringements. Among these, patent classification is an important task. It deals with the classification of patents into various categories. A common bottleneck in this task has been related to the automated classification of patents with better accuracy. The rapid increase in the number of patents being filed every year and the increasing complexity of the patent documents demand for advanced and revolutionized tools/machines to assist in performing patent classification in automated manner. Usually, the patents are examined thoroughly by patent analysts from various domains, who possess respective expertise and are well aware of the domain jargons. The main objective of such systems is to get rid of the time-consuming, laborious manual process and to provide patent analysts a better way for classifying patent documents. Also it helps in better management, maintenance and convenient searching of patent documents. Here, two prominent classification algorithms—Naïve Bayes and support vector machines (SVM)—are explored and implemented. Additionally, some pre-processing steps such as stop word removal, stemming, and lemmatizing are also done to obtain better accuracy. TF-IDF feature is also incorporated to obtain precise results.