Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative fea...

Efficiency, memory consumption, and robustness are common problems with many popular methods for data analysis. As a solution, we present Random Bits Forest (RBF), a classification and regression algorithm that integrates neural networks (for depth), boosting (for width), and random forests (for prediction accuracy). Through a gradient boosting scheme, it first generates and selects ~10,000 sma...

Machine learning is a concerned with the design and development of algorithms. Machine learning is a programming approach to computers to achieve optimization .Classification is the prediction approach in data mining techniques. Decision tree algorithm is the most common classifier to build tree because of it is easier to implement and understand. Attribute selection is a concept by which we wa...

Random trees planning has been used to solve connectivity problems in a variety of problem domains. In particular, these approaches are some of the most successful approaches to robotic motion planning. In this work we examine the broad array of random-tree that have been proposed for motion planning. From this survey we distill a general algorithmic framework for random-tree planning that abst...

Leo Breiman’s Random Forest ensemble learning procedure is applied to the problem of Quantitative Structure-Activity Relationship (QSAR) modeling for pharmaceutical molecules. This entails using a quantitative description of a compound’s molecular structure to predict that compound’s biological activity as measured in an in vitro assay. Without any parameter tuning, the performance of Random Fo...

Traditional approaches to the motion-planning problem can be classified into solutions for single-query and multiple-query problems with the tradeoffs on run-time computation cost and adaptability to environment changes. In this paper, we propose a novel approach to the problem that can learn incrementally on every planning query and effectively manage the learned road-map as the process goes o...

Random mappings from a nite set into itself are either a heuristic or an exact model for a variety of applications in random number generation, computational number theory, cryptography, and the analysis of algorithms at large. This paper introduces a general framework in which the analysis of about twenty characteristic parameters of random mappings is carried out: These parameters are studied...

Random forests are one of the best performing methods for constructing ensembles. They derive their strength from two aspects: using random subsamples of the training data (as in bagging) and randomizing the algorithm for learning base-level classifiers (decision trees). The base-level algorithm randomly selects a subset of the features at each step of tree construction and chooses the best amo...