In many regression and time series forecasting problems, the input data is not fully available at the beginning of the training phase. Conventional machine learning methods for batch data are not able to handle this problem. The sequential version of ELM, called Online Sequential Extreme Learning Machine (OS-ELM), addresses this problem through the least squares recursive solution for updating the network output weights. However, the implementation of OS-ELM and its extensions suffer from the problem of multicollinearity and its side effect on the variance of the weight estimates. This paper introduces a new method of sequential learning for handling the effects of multicollinearity. The proposed method, called Kalman Learning Machine (KLM), uses the Kalman filter to sequentially update the output weights of a Single Layer Feedforward Network (SLFN) based on OS-ELM. An extension of the proposed method, called Extended Kalman Learning Machine (EKLM), is presented in order to address the problem of nonlinear data. The proposed method was compared with some of the most recent and effective methods for handling the effects of multicollinearity in sequential learning problems. The experiments performed showed that the proposed method performs better than most state-of-the-art methods considering both the prediction error and training time.

Software development effort estimation is the process of predicting the effort required to develop a software system. In order to improve the estimation accuracy, many different models have been proposed in the literature. Multiple classification systems represent an important field of research for machine learning. In order to estimate software development effort, this paper proposes a heterogeneous and dynamic ensemble selection model, composed by a set of regressors dynamically selected by classifiers. Along with the proposed method it is conducted an experimental analysis involving a relevant set of software effort estimation problems, which has led to better results than those achieved by classical and state of the art models previously presented.

Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field. (C) 2016 Elsevier Ltd. All rights reserved.

In this paper, a new sequential learning algorithm is constructed by combining the Online Sequential Extreme Learning Machine (OS-ELM) and Kalman filter regression. The Kalman Online Sequential Extreme Learning Machine (KOSELM) handles the problem of multicollinearity of the OS-ELM, which can generate poor predictions and unstable models. The KOSELM learns the training data one-by-one or chunk-by-chunk by adjusting the variance of the output weights through the Kalman filter. The performance of the proposed algorithm has been validated on benchmark regression datasets, and the results show that KOSELM can achieve a higher learning accuracy than OS-ELM and its related extensions. A statistical validation for the differences of the accuracy for all algorithms is performed, and the results confirm that KOSELM has better stability than ReOS-ELM, TOSELM and LS-IELM.

In this paper we evaluate the combination of Extreme Learning Machine (ELM) and Support Vector Regression (SVR) with a Kalman filter regression model for financial time series forecasting. We also compare the forecast performance with a set of linear regression combination methods. The application of the traditional Kalman Filter for the statistical arbitrage strategy improves the statistical performance of ELM and SVR individual forecasts. The accuracy of the models is statistically tested and an investigation is performed to confirm the impact of the forecasts combination in terms of annualized returns and volatility.

In this paper we investigate the statistical and economic performance for statistical arbitrage strategy using Extreme Learning Machine (ELM) and Support Vector Regression (SVR) models, and their forecast combination through four linear combination models. The application of the traditional Kalman Filter for the statistical arbitrage strategy improves the statistical performance of ELM and SVR individual forecasts. It is presented evidence that the financial performance for most of cointegrated pairs can be improved by at least one linear combination technique.

It is increasingly common to use tools of Symbolic Data Analysis to reduce the data set without losing much information. Moreover, symbolic variables can be used to preserving the privacy of individuals when their information are present in the data set. In this work, we use information about researchers of institutions from Brazil through the tools of Symbolic Data Analysis and a weighted clustering method for interval data. The main goal is to analyze the scientific production of Brazilian institutions. Results of the cluster analysis and concluding remarks are given.

A large number of software organizations are adopting the software product line approach in their reuse program. One fundamental factor to evaluate cost-benefit of this approach is the practical use of cost models to estimate if an investment is worthwhile for a family of products. This paper analyzes the most significant cost models for product line engineering and it highlights the set of features that makes an effective model. This work also presents an integrated cost model for product line engineering with its foundations and elements. At the end, is presented a discussion over the results of a case study where the model was applied.

A large number of software organizations are adopting the software product line approach in their reuse program. One fundamental factor to evaluate cost-benefit of this approach is the practical use of cost models to estimate ROI to a family of products. This paper describes the state of art of cost models for software product lines, presenting the models evolution on the last decade. This work makes a summary of basic reuse cost models and addresses the use of reuse scenarios for ROI estimation. At the end, a framework for ROI estimation in product lines is specified through the definition of a Cost Configuration Model.