Bruno Feres de Souza

Multiclass SVM Model Selection Using Particle Swarm Optimization

Tuning SVM hyperparameters is an important step for achieving good classification performance. In the binary case, the model selection issue is well studied. For multiclass problems, it is harder to choose appropriate values for the base binary models of a decomposition scheme. In this paper, the authors employ Particle Swarm Optimization to perform a multiclass model selection, which optimizes the hyperparameters considering both local and globalmodels. Experiments conducted over 4 benchmark problems show promising results.

MetaStream: A meta-learning based method for periodic algorithm selection in time-changing data

Dynamic real-world applications that generate data continuously have introduced new challenges for the machine learning community, since the concepts to be learned are likely to change over time. In such scenarios, an appropriate model at a time point may rapidly become obsolete, requiring updating or replacement. As there are several learning algorithms available, choosing one whose bias suits the current data best is not a trivial task. In this paper, we present a meta-learning based method for periodic algorithm selection in time-changing environments, named MetaStream. It works by mapping the characteristics extracted from the past and incoming data to the performance of regression models in order to choose between single learning algorithms or their combination. Experimental results for two real regression problems showed that MetaStream is able to improve the general performance of the learning system compared to a baseline method and an ensemble-based approach.

Gene Selection Using Genetic Algorithms

Microarrays are emerging technologies that allow biologists to better understand the interactions between disease and normal states, at genes level. However, the amount of data generated by these tools becomes problematic when data are supposed to be automatically analyzed (e.g., for diagnostic purposes). In this work, the authors present a novel gene selection method based on Genetic Algorithms (GAs). The proposed method uses GAs to search for subsets of genes that optimize 2 measures of quality for the clusters presented in the domain. Thus, data are better represented and classification of unknown samples may become easier. In order to demonstrate the strength of the proposed approach, experimental results using 4 public available microarray datasets were carried out.

A comprehensive comparison of ML algorithms for gene expression data classification

Nowadays, microarray has become a fairly common tool for simultaneously inspecting the behavior of thousands of genes. Researchers have employed this technique to understand various biological phenomena. One straightforward use of such technology is identifying the class membership of the tissue samples based on their gene expression profiles. This task has been handled by a number of computational methods. In this paper, we provide a comprehensive evaluation of 7 commonly used algorithms over 65 publicly available gene expression datasets. The focus of the study was on comparing the performance of the algorithms in an efficient and sound manner, supporting the prospective users on how to proceed to choose the most adequate classification approach according to their investigation goals.

Empirical evaluation of ranking prediction methods for gene expression data classification

Recently, meta-learning techniques have been employed to the problem of algorithm recommendation for gene expression data classification. Due to their flexibility, the advice provided to the user was in the form of rankings, which are able to express a preference order of Machine Learning algorithms accordingly to their expected relative performance. Thus, choosing how to learn accurate rankings arises as a key research issue. In this work, the authors empirically evaluated 2 general approaches for ranking prediction and extended them. The results obtained for 49 publicly available microarray datasets indicate that the extensions introduced were very beneficial to the quality of the predicted rankings.

Meta‐learning approach to gene expression data classification

Purpose – The purpose of this paper is to investigate the applicability of meta‐learning to the problem of algorithm recommendation for gene expression data classification.Design/methodology/approach – Meta‐learning was used to provide a preference order of machine learning algorithms, based on their expected performances. Two approaches were considered for such: k‐nearest neighbors and support vector machine‐based ranking methods. They were applied to a set of 49 publicly available microarray datasets. The evaluation of the methods followed standard procedures suggested in the meta‐learning literature.Findings – Empirical evidences show that both ranking methods produce more interesting suggestions for gene expression data classification than the baseline method. Although the rankings are more accurate, a significant difference in the performances of the top classifiers was not observed.Practical implications – As the experiments conducted in this paper suggest, the use of meta‐learning approaches can pr...

Predicting execution time of machine learning tasks using metalearning

Lately, many academic and industrial fields have shifted research focus from data acquisition to data analysis. This transition has been facilitated by the usage of Machine Learning (ML) techniques to automatically identify patterns and extract non-trivial knowledge from data. The experimental procedures associated with that are usually complex and computationally demanding. Scheduling is a typical method used to decide how to allocate tasks into available resources. An important step for such is to guess how long an application would take to execute. In this paper, we introduce an approach for predicting processing time specifically of ML tasks. It employs a metalearning framework to relate characteristics of datasets and current machine state to actual execution time. An empirical study was conducted using 78 publicly available datasets, 6 ML algorithms and 4 meta-regressors. Experimental results show that our approach outperforms a commonly used baseline method. Statistical tests advise using SVMr as meta-regressor. These achievements indicate the potential of metalearning to tackle the problem and encourage further developments.

Predicting execution time of machine learning tasks for scheduling

Lately, many academic and industrial fields have shifted their research focus from data acquisition to data analysis. This transition has been facilitated by the usage of Machine Learning ML techniques to automatically identify patterns and extract non-trivial knowledge from data. The experimental procedures associated with that are usually complex and computationally demanding. To deal with such scenario, Distributed Heterogeneous Computing DHC systems can be employed. In order to fully benefit from DHT facilities, a suitabble scheduling policy should be applied to decide how to allocate tasks into the available resources. An important step for such is to guess how long an application would take to execute. In this paper, we present an approach for predicting execution time specifically of ML tasks. It employs a metalearning framework to relate characteristics of datasets and current machine state to actual execution time. An empirical study was conducted using 78 publicly available datasets, 6 ML algorithms and 4 meta-regressors. Experimental results show that our approach outperforms a commonly used baseline method. After establishing SVM as the most promising meta-regressor, we employed its predictions to actually build schedule plans. In a simulation considering a small scale DHC enviroment, a simple Genetic Algorithm based scheduler was employed for task allocation, leading to minimized overall completion time. These achievements indicate the potential of meta-learning to tackle the problem and encourage further developments.

Using genetic algorithms to improve prediction of execution times of ML tasks

Experimental procedures associated with Machine Learning (ML) techniques are usually computationally demanding. An important step for a conscientious allocation of ML tasks into resources is predicting their execution times. Previously, empirical comparisons using a Meta-learning framework indicated that Support Vector Machines (SVM) are suited for this problem; however, their performance is affected by the choice of parameter values and input features. In this paper, we tackle the issue by applying Genetic Algorithm (GA) to perform joint Feature Subset Selection (FSS) and Parameters Optimization (PO). At first, a GA is used for FSS+PO in SVMs with two kernel functions, independently. Later, besides FSS+PO an additional term is evolved to weight predictions of both models to build a combined regressor. An empirical investigation conducted for predicting execution times of 6 ML algorithms over 78 publicly available datasets unveils a higher accuracy when compared with the previous results.

Metalearning for Gene Expression Data Classification

Machine Learning techniques have been largely applied to the problem of class prediction in microarray data. Nevertheless, current approaches to select appropriate methods for such task often result unsatisfactory in many ways, instigating the need for the development of tools to automate the process. In this context, the authors introduce the use of metalearning in the specific domain of gene expression classification. Experiments with the KNN-ranking method for algorithm recommendation applied for 49 datasets yielded successful results.