A novel approach to ensemble learning in distributed data mining
نویسندگان
چکیده
منابع مشابه
SWITCH: A Novel Approach to Ensemble Learning for Heterogeneous Data
The standard framework of machine learning problems assumes that the available data is independent and identically distributed (i.i.d.). However, in some applications such as image classification, the training data are often collected from multiple sources and heterogeneous. Ensemble learning is a proven effective approach to heterogeneous data, which uses multiple classification models to capt...
متن کاملUsing Mining@Home for Distributed Ensemble Learning
Mining@Home was recently designed as a distributed architecture for running data mining applications according to the “volunteer computing” paradigm. Mining@Home already proved its efficiency and scalability when used for the discovery of frequent itemsets from a transactional database. However, it can also be adopted in several different scenarios, especially in those where the overall applica...
متن کاملDistributed Learning: An Agent-Based Approach to Data-Mining
This extended abstract summarises our current research which spans the fields of knowledge discovery and software agents. Knowledge discovery (or data-mining) is concerned with extracting knowledge from databases and/or knowledge bases (Piatetsky-Shapiro & Frawley, 1991) using machine learning techniques. Traditionally, data-mining systems are designed to work on a single dataset. However, with...
متن کاملData mining for algorithmic asset management: an ensemble learning approach
Algorithmic asset management refers to the use of expert systems that enter trading orders without any user intervention. In particular, market-neutral systems aim at generating positive returns regardless of underlying market conditions. In this chapter we describe an incremental learning framework for algorithmic asset management based on support vector regression. The algorithm learns the fa...
متن کاملLearning FCM by Data Mining in a Purchase System
Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show the relations between essential components in complex systems. In this paper, a novel learning method is proposed to construct FCMs based on historical data and by using meta-heuristic: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). Implementation of the proposed method has demonstrat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Engineering & Technology
سال: 2018
ISSN: 2227-524X
DOI: 10.14419/ijet.v7i2.33.14159