A Comparison of Machine Learning Classifiers Applied to Financial Datasets
نویسندگان
چکیده
*Abstract—The main purpose of this project is to analyze several Machine Learning techniques individually and compare the efficiency and classification accuracy of those techniques. Three algorithms are used (Naïve Bayes learning, feed forward Artificial Neural Networks with Backpropagation, and Decision Trees learning using C4.5) over two datasets (“European companies” and “Japanese companies”) characterized by 59 financial features each.
منابع مشابه
A Classification Method for E-mail Spam Using a Hybrid Approach for Feature Selection Optimization
Spam is an unwanted email that is harmful to communications around the world. Spam leads to a growing problem in a personal email, so it would be essential to detect it. Machine learning is very useful to solve this problem as it shows good results in order to learn all the requisite patterns for classification due to its adaptive existence. Nonetheless, in spam detection, there are a large num...
متن کاملA Pre-Trained Ensemble Model for Breast Cancer Grade Detection Based on Small Datasets
Background and Purpose: Nowadays, breast cancer is reported as one of the most common cancers amongst women. Early detection of the cancer type is essential to aid in informing subsequent treatments. The newest proposed breast cancer detectors are based on deep learning. Most of these works focus on large-datasets and are not developed for small datasets. Although the large datasets might lead ...
متن کاملApplication of ensemble learning techniques to model the atmospheric concentration of SO2
In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...
متن کاملدستهبندی پرسشها با استفاده از ترکیب دستهبندها
Question answering systems are produced and developed to provide exact answers to the question posted in natural language. One of the most important parts of question answering systems is question classification. The purpose of question classification is predicting the kind of answer needed for the question in natural language. The literature works can be categorized as rule-based and learning...
متن کاملMammalian Eye Gene Expression Using Support Vector Regression to Evaluate a Strategy for Detecting Human Eye Disease
Background and purpose: Machine learning is a class of modern and strong tools that can solve many important problems that nowadays humans may be faced with. Support vector regression (SVR) is a way to build a regression model which is an incredible member of the machine learning family. SVR has been proven to be an effective tool in real-value function estimation. As a supervised-learning appr...
متن کامل