A machine learning system is useful for extracting models from data that can be used for many applications such as data analysis , decision support or data mining. SMILES is a machine learning system that integrates many diierent features from other machine learning techniques and paradigms, and more importantly, it presents several innovations in almost all of these features, such as ensemble ...

Extreme Learning Machine is a fast single layer feed forward neural network for real valued classification. It suffers from the problem of instability and over fitting. Voting based Extreme Learning Machine, VELM reduces this performance variation in Extreme Learning Machine by employing majority voting based ensembling technique. VELM improves the performance of ELM at the cost of increased re...

Traditionally, supervised machine learning methods are the first choice for tasks involving classification of data. This study provides a non-conventional hybrid alternative technique (pEAC) that blends the Possibilistic Fuzzy CMeans (PFCM) as base cluster generating algorithm into the ‘standard’ Evidence Accumulation Clustering (EAC) clustering method. The PFCM coalesces the separate propertie...

Credit scoring is an important finance activity. Both statistical techniques and Artificial Intelligence (AI) techniques have been explored for this topic. But different techniques have different advantages and disadvantages on different datasets. Recent studies draw no consistent conclusions to show that one technique is superior to the other, while they suggest combining multiple classifiers,...

Ensemble techniques are a powerful approach to creating better decision makers in machine learning. Multiple decision makers are trained to solve a given task, grouped in an ensemble, and their decisions are aggregated. The ensemble derives its power from the diversity of its components, as the assumption is that they make mistakes on different inputs, and that the majority is more likely to be...

Despite the fact that ensemble meta-learning of a heterogeneous collection of base learners is an effective means to reduce the generalization error in predictive models, several factors have impeded a broad adoption of such techniques among practitioners. These factors include an intractable number of choices of base learners and their tuning parameters, complex methodology required for integr...

In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...

Recently, multi-instance classification algorithm BP-MIP and multi-instance regression algorithm BP-MIR both based on neural networks have been proposed. In this paper, neural network ensemble techniques are introduced to solve multi-instance learning problems, where BP-MIP ensemble and BP-MIR ensemble are constructed respectively. Experiments on benchmark and artificial data sets show that ens...

For a successful project development, it is important for any software organization that the project should be completed within time and budget, and the project should have requisite quality. This paper presents an Ensemble learning based Adaptive Neuro-Fuzzy Approach for Software Development Time Estimation. The concept behind this technique is based on ensemble learning methods. This techniqu...

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...