Pairwise meta-rules for better meta-learning-based algorithm ranking

In this paper, we present a novel meta-feature generation method in the context of meta-learning, which is based on rules that compare the performance of individual base learners in a one-against-one manner. In addition to these new meta-features, we also introduce a new meta-learner called Approximate Ranking Tree Forests (ART Forests) that performs very competitively when compared with several state-of-the-art meta-learners. Our experimental results are based on a large collection of datasets and show that the proposed new techniques can improve the overall performance of meta-learning for algorithm ranking significantly. A key point in our approach is that each performance figure of any base learner for any specific dataset is generated by optimising the parameters of the base learner separately for each dataset

Gene selection and classification in autism gene expression data

Autism spectrum disorders (ASD) are neurodevelopmental disorders that are currently diagnosed on the basis of abnormal stereotyped behaviour as well as observable deficits in communication and social functioning. Although a variety of candidate genes have been attributed to the disorder, no single gene is applicable to more than 1–2% of the general ASD population. Despite extensive efforts, definitive genes that contribute to autism susceptibility have yet to be identified. The major problems in dealing with the gene expression dataset of autism include the presence of limited number of samples and large noises due to errors of experimental measurements and natural variation. In this study, a systematic combination of three important filters, namely t-test (TT), Wilcoxon Rank Sum (WRS) and Feature Correlation (COR) are applied along with efficient wrapper algorithm based on geometric binary particle swarm optimization-support vector machine (GBPSO-SVM), aiming at selecting and classifying the most attributed genes of autism. A new approach based on the criterion of median ratio, mean ratio and variance deviations is also applied to reduce the initial dataset prior to its involvement. Results showed that the most discriminative genes that were identified in the first and last selection steps concluded the presence of a repetitive gene (CAPS2), which was assigned as the most ASD risk gene. The fused result of genes subset that were selected by the GBPSO-SVM algorithm increased the classification accuracy to about 92.10%, which is higher than those reported in literature for the same autism dataset. Noticeably, the application of ensemble using random forest (RF) showed better performance compared to that of previous studies. However, the ensemble approach based on the employment of SVM as an integrator of the fused genes from the output branches of GBPSO-SVM outperformed the RF integrator. The overall improvement was ascribed to the selection strategies that were taken to reduce the dataset and the utilization of efficient wrapper based GBPSO-SVM algorithm

Feature weighting techniques for CBR in software effort estimation studies: A review and empirical evaluation

Context : Software effort estimation is one of the most important activities in the software development process. Unfortunately, estimates are often substantially wrong. Numerous estimation methods have been proposed including Case-based Reasoning (CBR). In order to improve CBR estimation accuracy, many researchers have proposed feature weighting techniques (FWT). Objective: Our purpose is to systematically review the empirical evidence to determine whether FWT leads to improved predictions. In addition we evaluate these techniques from the perspectives of (i) approach (ii) strengths and weaknesses (iii) performance and (iv) experimental evaluation approach including the data sets used. Method: We conducted a systematic literature review of published, refereed primary studies on FWT (2000-2014). Results: We identified 19 relevant primary studies. These reported a range of different techniques. 17 out of 19 make benchmark comparisons with standard CBR and 16 out of 17 studies report improved accuracy. Using a one-sample sign test this positive impact is significant (p = 0:0003). Conclusion: The actionable conclusion from this study is that our review of all relevant empirical evidence supports the use of FWTs and we recommend that researchers and practitioners give serious consideration to their adoption

A hybrid swarm intelligence feature selection approach based on time-varying transition parameter

Feature selection aims to reduce the dimensionality of a dataset by removing superfluous attributes. This paper proposes a hybrid approach for feature selection problem by combining particle swarm optimization (PSO), grey wolf optimization (GWO), and tournament selection (TS) mechanism. Particle swarm enhances the diversification at the beginning of the search mechanism, grey wolf enhances the intensification at the end of the search mechanism, while tournament selection maintains diversification not only at the beginning but also at the end of the search process to achieve local optima avoidance. A time-varying transition parameter and a random variable are used to select either particle swarm, grey wolf, or tournament selection techniques during search process. This paper proposes different variants of this approach based on S-shaped and V-shaped transfer functions (TFs) to convert continuous solutions to binaries. These variants are named hybrid tournament grey wolf particle swarm (HTGWPS), followed by S or V letter to indicate the TF type, and followed by the TF’s number. These variants were evaluated using nine high-dimensional datasets. The results revealed that HTGWPS-V1 outperformed other V’s variants, PSO, and GWO on 78% of the datasets based on maximum classification accuracy obtained by a minimal feature subset. Also, HTGWPS-V1 outperformed six well-known-metaheuristics on 67% of the datasets