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Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces Machine Learning

Both neural networks and decision trees are popular machine learning methods and are widely used to solve problems from diverse domains. These two classifiers are commonly used base classifiers in an ensemble framework. In this paper, we first present a new variant of oblique decision tree based on a linear classifier, then construct an ensemble classifier based on the fusion of a fast neural network, random vector functional link network and oblique decision trees. Random Vector Functional Link Network has an elegant closed form solution with extremely short training time. The neural network partitions each training bag (obtained using bagging) at the root level into C subsets where C is the number of classes in the dataset and subsequently, C oblique decision trees are trained on such partitions. The proposed method provides a rich insight into the data by grouping the confusing or hard to classify samples for each class and thus, provides an opportunity to employ fine-grained classification rule over the data. The performance of the ensemble classifier is evaluated on several multi-class datasets where it demonstrates a superior performance compared to other state-of- the-art classifiers.

How the random forest algorithm works in machine learning 7wData


You are going to learn the most popular classification algorithm. Which is the Random forest algorithm. As a motivation to go further I am going to give you one of the best advantages of random forest. The Same algorithm both for classification and regression, You mind be thinking I am kidding. But the truth is, Yes we can use the same random forest algorithm both for classification and regression.

A Comprehensive Guide to Ensemble Learning (with Python codes) - Analytics Vidhya


When you want to purchase a new car, will you walk up to the first car shop and purchase one based on the advice of the dealer? You would likely browser a few web portals where people have posted their reviews and compare different car models, checking for their features and prices. You will also probably ask your friends and colleagues for their opinion. In short, you wouldn't directly reach a conclusion, but will instead make a decision considering the opinions of other people as well. Ensemble models in machine learning operate on a similar idea. They combine the decisions from multiple models to improve the overall performance.

Bagging and Random Forest Ensemble Algorithms for Machine Studying


Random Forest is 1 of the most preferred and most highly effective equipment discovering algorithms. It is a style of ensemble equipment discovering algorithm referred to as Bootstrap Aggregation or bagging. In this publish you will explore the Bagging ensemble algorithm and the Random Forest algorithm for predictive modeling. This publish was published for developers and assumes no background in studies or mathematics. The publish focuses on how the algorithm performs and how to use it for predictive modeling complications.

How the random forest algorithm works in machine learning


If you are not aware of the concepts of decision tree classifier, Please spend some time on the below articles, As you need to know how the Decision tree classifier works before you learning the working nature of the random forest algorithm. Given the training dataset with targets and features, the decision tree algorithm will come up with some set of rules. In decision tree algorithm calculating these nodes and forming the rules will happen using the information gain and gini index calculations. In random forest algorithm, Instead of using information gain or gini index for calculating the root node, the process of finding the root node and splitting the feature nodes will happen randomly.