Hierarchical meta-rules for scalable meta-learning

The Pairwise Meta-Rules (PMR) method proposed in [18] has been shown to improve the predictive performances of several metalearning algorithms for the algorithm ranking problem. Given m target objects (e.g., algorithms), the training complexity of the PMR method with respect to m is quadratic: (formula presented). This is usually not a problem when m is moderate, such as when ranking 20 different learning algorithms. However, for problems with a much larger m, such as the meta-learning-based parameter ranking problem, where m can be 100+, the PMR method is less efficient. In this paper, we propose a novel method named Hierarchical Meta-Rules (HMR), which is based on the theory of orthogonal contrasts. The proposed HMR method has a linear training complexity with respect to m, providing a way of dealing with a large number of objects that the PMR method cannot handle efficiently. Our experimental results demonstrate the benefit of the new method in the context of meta-learning

Bagging ensemble selection

Ensemble selection has recently appeared as a popular ensemble learning method, not only because its implementation is fairly straightforward, but also due to its excellent predictive performance on practical problems. The method has been highlighted in winning solutions of many data mining competitions, such as the Netix competition, the KDD Cup 2009 and 2010, the UCSD FICO contest 2010, and a number of data mining competitions on the Kaggle platform. In this paper we present a novel variant: bagging ensemble selection. Three variations of the proposed algorithm are compared to the original ensemble selection algorithm and other ensemble algorithms. Experiments with ten real world problems from diverse domains demonstrate the benefit of the bagging ensemble selection algorithm

Evolving artificial datasets to improve interpretable classifiers

Differential Evolution can be used to construct effective and compact artificial training datasets for machine learning algorithms. In this paper, a series of comparative experiments are performed in which two simple interpretable supervised classifiers (specifically, Naive Bayes and linear Support Vector Machines) are trained (i) directly on “real” data, as would be the normal case, and (ii) indirectly, using special artificial datasets derived from real data via evolutionary optimization. The results across several challenging test problems show that supervised classifiers trained indirectly using our novel evolution-based approach produce models with superior predictive classification performance. Besides presenting the accuracy of the learned models, we also analyze the sensitivity of our artificial data optimization process to Differential Evolution's parameters, and then we examine the statistical characteristics of the artificial data that is evolved

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

Towards a framework for designing full model selection and optimization systems

People from a variety of industrial domains are beginning to realise that appropriate use of machine learning techniques for their data mining projects could bring great benefits. End-users now have to face the new problem of how to choose a combination of data processing tools and algorithms for a given dataset. This problem is usually termed the Full Model Selection (FMS) problem. Extended from our previous work [10], in this paper, we introduce a framework for designing FMS algorithms. Under this framework, we propose a novel algorithm combining both genetic algorithms (GA) and particle swarm optimization (PSO) named GPS (which stands for GA-PSO-FMS), in which a GA is used for searching the optimal structure for a data mining solution, and PSO is used for searching optimal parameters for a particular structure instance. Given a classification dataset, GPS outputs a FMS solution as a directed acyclic graph consisting of diverse data mining operators that are available to the problem. Experimental results demonstrate the benefit of the algorithm. We also present, with detailed analysis, two model-tree-based variants for speeding up the GPS algorithm

Full model selection in the space of data mining operators

We propose a framework and a novel algorithm for the full model selection (FMS) problem. The proposed algorithm, combining both genetic algorithms (GA) and particle swarm optimization (PSO), is named GPS (which stands for GAPSO-FMS), in which a GA is used for searching the optimal structure of a data mining solution, and PSO is used for searching the optimal parameter set for a particular structure instance. Given a classification or regression problem, GPS outputs a FMS solution as a directed acyclic graph consisting of diverse data mining operators that are applicable to the problem, including data cleansing, data sampling, feature transformation/selection and algorithm operators. The solution can also be represented graphically in a human readable form. Experimental results demonstrate the benefit of the algorithm