Classification in supervised learning is a way to find patterns data base that the classes are already known. In classification of machine learning, there term called ensemble classifier. The workings classifier aimed improve model accuracy and optimize performance. This study aims analyze comparison algorithms work with , including Random Forest, Support Vector Machine (SVM), AdaBoost. used Hu...

This paper demonstrates the effectiveness of ensemble machine learning algorithms over the conventional multivariable linear regression models including Ordinary Least Squares, Robust Linear Model, and Lasso Model. The ensemble machine learning algorithms include Adaboost, Random-Forest, Bagging, Extremely Randomized Trees, Gradient Boosting, and Extra Trees Regressor. With the progress of open...

We study the classical AdaBoost algorithm in the context of fairness. We use the Census Income Dataset (Lichman, 2013) as a case study. We empirically evaluate the bias and error of four variants of AdaBoost relative to an unmodified AdaBoost baseline, and study the trade-offs between reducing bias and maintaining low error. We further define a new notion of fairness and measure it for all of o...

In this paper we present a particular implementation of the Learn++ algorithm: we investigate the predictability of financial movement direction with Learn++ by forecasting the daily movement direction of the Dow Jones. The Learn++ algorithm is derived from the Adaboost algorithm, which is denominated by sub-sampling. The goal of concept learning, according to the probably approximately correct...

While boosting has been extensively studied, considerably less attention has been devoted to the task of designing good weak learning algorithms. In this paper we consider the problem of designing weak learners that are especially adept to the boosting procedure and specifically the AdaBoost algorithm. First we describe conditions desirable for a weak learning algorithm. We then propose using s...

Ensemble learning has been shown to be very successful in data mining. However most work on ensemble learning concerns the task of classification. Little work has been done to construct ensembles that aim to improve ranking. In this paper, we propose an approach to re-construct new ensembles based on a given ensemble with the purpose to improve the ranking performance, which is crucial in many ...

In this paper, we argue that only using behavioural motion information, we are able to predict the interest of observers when looking at face-to-face interactions. We propose a set of movement-related features from body, face, and mouth activity in order to define a set of higher level interaction features, such as stress, activity, speaking engagement, and corporal engagement. Error-Correcting...

This paper shows that new and flexible criteria to resample populations in boosting algorithms can lead to performance improvements. Real Adaboost emphasis function can be divided into two different terms, the first only pays attention to the quadratic error of each pattern and the second takes only into account the “proximity” of each pattern to the boundary. Here, we incorporate an additional...

Previous work has high classification accuracy when classifying music genres that are very different [1] (e.g. rock vs. pop, jazz vs. classical), but little machine learning research has been done to classify music by subgenres within a larger genre that describe temporally and stylistically similar music. In this paper, we apply machine learning to classify classical music by time period. To d...