In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We present an adaptation of several popular ensemble techniques in machine learnin...

Deep learning practices in the agriculture sector can address many challenges faced by farmers such as disease detection, yield estimation, soil profile etc. In this paper, classification for sugarcane plant and experimentation involved thereby is thoroughly discussed. Experimental results include performances of well-known existing transfer techniques proposed ensemble deep based architecture ...

We investigate the theoretical links between a regression ensemble and a linearly combined classification ensemble. First, we reformulate the Tumer & Ghosh model for linear combiners in a regression context; we then exploit this new formulation to generalise the concept of the “Ambiguity decomposition”, previously defined only for regression tasks, to classification problems. Finally, we propos...

Ensemble learning methods have received remarkable attention in the recent years and led to considerable advancement in the performance of the regression and classification problems. Bagging and boosting are among the most popular ensemble learning techniques proposed to reduce the prediction error of learning machines. In this study, bagging and gradient boosting algorithms are incorporated in...

From the beginning of machine learning, rule induction has been regarded as one of the most important issues in this research area. One of the first rule induction algorithms was AQ introduced by Michalski in early 80’s. AQ, as well as several other well-known algorithms, such as CN2 and Ripper, are all based on sequential covering. With the advancement of machine learning, some new techniques ...

This study proposes two techniques: Deep Learning (DL) and Ensemble (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 was used as inputs the fifth well scenario-2 (S2): time series with lag up 20 days all wells. The results S1 prove that ensemble EDL generally performs superior DL estimation of each station using data r...

Over the past few years, there has been a significant increase in interest and adoption of solar energy all over world. However, despite ongoing efforts to protect photovoltaic (PV) plants, they are continuously exposed numerous anomalies. If not detected accurately timely manner, anomalies PV plants may degrade desired performance result severe consequences. Hence, developing effective flexibl...

Distributed data mining and ensemble learning are two methods that aim to address the issue of data scaling, which is required to process the large amount of data collected these days. Distributed data mining looks at how data that is distributed can be effectively mined without having to collect the data at one central location. Ensemble learning techniques aim to create a meta-classifier by c...

In this study, the ensemble classifier presented by Caruana, Niculescu-Mizil, Crew & Ksikes (2004) is investigated. Their ensemble approach generates thousands of models using a variety of machine learning algorithms and uses a forward stepwise selection to build robust ensembles that can be optimised to an arbitrary metric. On average, the resulting ensemble out-performs the best individual ma...