Classification with Extreme Learning Machine and Ensemble Algorithms Over Randomly Partitioned Data
In this age of Big Data, machine learning based data mining methods are extensively used to inspect large scale data sets. Deriving applicable predictive modeling from these type of data sets is a challenging obstacle because of their high complexity. Opportunity with high data availability levels, automated classification of data sets has become a critical and complicated function. In this paper, the power of applying MapReduce based Distributed AdaBoosting of Extreme Learning Machine (ELM) are explored to build reliable predictive bag of classification models. Thus, (i) dataset ensembles are build; (ii) ELM algorithm is used to build weak classification models; and (iii) build a strong classification model from a set of weak classification models. This training model is applied to the publicly available knowledge discovery and data mining datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationSimilar Papers 제목 키워드 기반
Single classifier vs. ensemble machine learning approaches for mental health prediction
Early prediction of mental health issues among individuals is paramount for early diagnosis and treatment by mental health professionals. One of the promising approaches to achieving fully automated computer-based appro…
Crystal Systems Classification of Phosphate-Based Cathode Materials Using Machine Learning for Lithium-Ion Battery
The physical and chemical characteristics of cathodes used in batteries are derived from the lithium-ion phosphate cathodes crystalline arrangement, which is pivotal to the overall battery performance. Therefore, the cor…
Robust OS-ELM with a novel selective ensemble based on particle swarm optimization
In this paper, a robust online sequential extreme learning machine (ROS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. F…
General ClassificationEnsemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection
Machine Learning-based supervised approaches require highly customized and fine-tuned methodologies to deliver outstanding performance. This paper presents a dataset-driven design and performance evaluation of a machine …
ClassificationIntrusion DetectionNetwork Intrusion DetectionInterpretable Ensembles of Hyper-Rectangles as Base Models
A new extremely simple ensemble-based model with the uniformly generated axis-parallel hyper-rectangles as base models (HRBM) is proposed. Two types of HRBMs are studied: closed rectangles and corners. The main idea behi…
Computational Efficiency