Knowledge Discovery in Databases is a complex process that involves many different data processing and learning operators. Today's Knowledge Discovery Support Systems can contain several hundred operators. A major challenge is to assist the user in designing workflows which are not only valid but also -- ideally -- optimize some performance measure associated with the user goal. In this paper we present such a system. The system relies on a meta-mining module which analyses past data mining experiments and extracts meta-mining models which associate dataset characteristics with workflow descriptors in view of workflow performance optimization. The meta-mining model is used within a data mining workflow planner, to guide the planner during the workflow planning. We learn the meta-mining models using a similarity learning approach, and extract the workflow descriptors by mining the workflows for generalized relational patterns accounting also for domain knowledge provided by a data mining ontology. We evaluate the quality of the data mining workflows that the system produces on a collection of real world datasets coming from biology and show that it produces workflows that are significantly better than alternative methods that can only do workflow selection and not planning.

The notion of meta-mining has appeared recently and extends traditional meta-learning in two ways. First it provides support for the whole data-mining process. Second it pries open the so called algorithm black-box approach where algorithms and workflows also have descriptors. With the availability of descriptors both for datasets and data-mining workflows we are faced with a problem the nature of which is much more similar to those appearing in recommendation systems. In order to account for the meta-mining specificities we derive a novel metric-based-learning recommender approach. Our method learns two homogeneous metrics, one in the dataset and one in the workflow space, and a heterogeneous one in the dataset-workflow space. All learned metrics reflect similarities established from the dataset-workflow preference matrix. The latter is constructed from the performance results obtained by the application of workflows to datasets. We demonstrate our method on meta-mining over biological (microarray datasets) problems. The application of our method is not limited to the meta-mining problem, its formulation is general enough so that it can be applied on problems with similar requirements.

In this article we present a novel kernel function, logRatio, which was designed to address two common problems in biological applications: data preprocessing and attribute interaction modelling. An extension of the SVMRFE feature selection algorithm was built around this new kernel function and compared with the original on a number of biological data and text classification problems. Experiments showed that SVMRFE based on the logRatio kernel detects relevant information and handles attribute redundancy more effectively than SVMRFE coupled with other kernels.

Mass-spectra based proteomic profiles have received widespread attention as potential tools for biomarker discovery and early disease diagnosis. A major data-analytical problem involved is the extremely high dimensionality (i.e. number of features or variables) of proteomic data, in particular when the sample size is small. This article reviews dimensionality reduction methods that have been used in proteomic biomarker studies. It then focuses on the problem of selecting the most appropriate method for a specific task or dataset, and proposes method combination as a potential alternative to single-method selection. Finally, it points out the potential of novel dimension reduction techniques, in particular those that incorporate domain knowledge through the use of informative priors or causal inference.

The k-Nearest Neighbors algorithm can be easily adapted to classify complex objects (e.g. sets, graphs) as long as a proper dissimilarity function is given over an input space. Both the representation of the learning instances and the dissimilarity employed on that representation should be determined on the basis of domain knowledge. However, even in the presence of domain knowledge, it can be far from obvious which complex representation should be used or which dissimilarity should be applied on the chosen representation. In this paper we present a framework that allows to combine different complex representations of a given learning problem and/or different dissimilarities defined on these representations. We build on ideas developed previously on metric learning for vectorial data. We demonstrate the utility of our method in domains in which the learning instances are represented as sets of vectors by learning how to combine different set distance measures.

Mass spectrometry is becoming an important tool in proteomics. Mass spectral data are characterized by very high dimensionality and a high level of redundancy. Both issues are quite challenging when one wants to perform knowledge discovery and push existing tools to their limits. We tackle both via a preprocessing pipeline that drastically reduces dimensionality and redundancy of the initial representation in order to focus on biologically relevant information. Essentially preprocessing performs feature extraction in a manner that reflects domain knowledge. We propose a framework for the evaluation of the given pipeline and in fact of any mass spectrometry preprocessing pipeline which is based on the level of conservation of discriminatory information. The discriminatory information content of a given representation is objectively measured by the classification performance of a number of classification algorithms evaluated on the given representation. This approach also allows us to compare a number of different preprocessing possibilities, namely using peak intensities vs peak areas to represent peaks and how non observed peaks should be treated, and demonstrate which is the most informative one

The main disadvantage of most existing set kernels is that they are based on averaging, which might be inappropriate for problems where only specific elements of the two sets should determine the overall similarity. In this paper we propose a class of kernels for sets of vectors directly exploiting set distance measures and, hence, incorporating various semantics into set kernels and lending the power of regularization to learning in structural domains where natural distance functions exist. These kernels belong to two groups: (i) kernels in the proximity space induced by set distances and (ii) set distance substitution kernels (non-PSD in general). We report experimental results which show that our kernels compare favorably with kernels based on averaging and achieve results similar to other state-of-the-art methods. At the same time our kernels systematically improve over the naive way of exploiting distances.

With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected. This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set. We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset. We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them. We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets. The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms. Finally we show how stability profiles can support the choice of a feature selection algorithm.

SUMMARY: METIS is a web-based integrated annotation tool. From single query sequences, the PRECIS component allows users to generate structured protein family reports from sets of related Swiss-Prot entries. These reports may then be augmented with pertinent sentences extracted from online biomedical literature via support vector machine and rule-based sentence classification systems. AVAILABILITY: http://umber.sbs.man.ac.uk/dbbrowser/metis/

We present work on a proteomics application. More specifically, from the domain of mass-spectrometry and the identification of biomarkers for stroke attacks. Mass-spectrometry based biomarker identification is an application that sets a number of challenges to the knowledge discovery process. We describe how we tackle them and present a number of machine learning experiments that we performed in order to identify the most suitable learning algorithm for the given problem. However working with real world applications one of the main issues apart from good classification performance is an indication of the factors that really determine the classification decision. Usually based on the results of a resampled-based performance estimation, e.g. cross validation, an algorithm is selected that will provide the operational classification model. On a next step the operational model should be constructed, nevertheless it is not obvious how this should be done since in resampled-based procedures a number of different models are created. We propose a method for linear classifiers that examines the different models produced with cross-validation. The method examines the stability of the models produced from the different training folds and combines them to provide a single model.

The selection of an appropriate inducer is crucial for performing effective classification. In previous work we presented a system called NOEMON which relied on a mapping between dataset characteristics and inducer performance to propose inducers for specific datasets. Instance based learning was used to create that mapping. Here we extend and refine the set of data characteristics; we also use a wider range of base-level inducers and a much larger collection of datasets to create the meta-models. We compare the performance of meta-models produced by instance based learners, decision trees and boosted decision trees. The results show that decision trees and boosted decision trees models enhance the perfomance of the system.

Proposes an original approach to musical score recognition, a particular case of high-level document analysis. In order to overcome the limitations of existing systems, we propose an architecture which allows for a continuous and bidirectional interaction between high-level knowledge and low-level data, and which is able to improve itself over time by learning. This architecture is made of three cooperating layers, one made of parameterized feature detectors, another working as an object-oriented knowledge repository and the other as a supervising Bayesian metaprocessor. Although the implementation is still in progress, we show how this architecture is adequate for modeling and processing knowledge.

In this paper, the term knowledge-based neural network (NN) design is used to refer to all efforts at exploiting prior knowledge in neural network configuration and training. A variety of techniques have been proposed for this purpose; SCANDAL provides a workbench for evaluating and integrating these techniques. After a quick overview of three main approaches to NN design, we describe SCANDALS multi-agent, metalevel architecture as well as its strategies for maximizing the use of domain knowledge. To assess the impact of prior knowledge on NN performance, experiments were conducted comparing knowledge-based techniques with a search-based configuration algorithm. Results show that the use of prior knowledge in neural network design leads to both faster learning and improved generalization. More interestingly, this appears to hold even when domain knowledge and data are deficient; in such cases, knowledge is extracted from the available data and is used both to configure the network and to generate artificial training instances. This leads us to hope that time-consuming iterative search can be avoided even in knowledge-lean domains.