Abstract With an expected torrent of data from the Vera C. Rubin Observatory Legacy Survey Space and Time (LSST), need for automated identification noisy sparse light curves will increase drastically. In this paper, we performed classification multiband astronomical Photometric LSST Astronomical Time-series Classification Challenge ( PLAsTiCC ) set via boosted neural nets, decision trees, a vot...

The advent of gene chips has led to a promising technology for cell, tumor, and cancer classification. We exploit and expand the methodology of recursive partitioning trees for tumor and cell classification from microarray gene expression data. To improve classification and prediction accuracy, we introduce a deterministic procedure to form forests of classification trees and compare their perf...

The extraction and exploitation of existing knowledge assets for supporting decision making and increasing the effectiveness of various internal and external interventions is of critical importance for the success of modern organizations. The use of advanced Operational Research-based quantitative methods in combination with high capabilities information systems can be very useful for this purp...

The last decade has witnessed a growing interest in random forest models which are recognized to exhibit good practical performance, especially in high-dimensional settings. On the theoretical side, however, their predictive power remains largely unexplained, thereby creating a gap between theory and practice. The aim of this paper is twofold. Firstly, we provide theoretical guarantees to link ...

Abstract We compare the performance of a wide set regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using private database from European debt collection agency. find that rule-based such as Cubist, boosted trees, random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors ref...

Scene-context plays an important role in scene analysis and object recognition. Among various sources of scene-context, we focus on scene-context scale, which means the effective scale of local context to classify an image pixel in a scene. This paper presents random forests based image categorization using the scene-context scale. The proposed method uses random forests, which are ensembles of...

Network traffic classification continues to be an interesting subject among numerous networking communities. This method introduces multi-beneficial solutions in different avenues, such as network security, network management, anomaly detection, and quality-of-service. In this paper, we propose a supervised machine learning method that efficiently classifies different types of applications usin...