Boosting of least-squares support vector machine (LS-SVM) algorithms can classify large datasets on standard personal computers (PCs). We extend the LS-SVM proposed by Suykens and Vandewalle in several ways to efficiently classify large datasets. We developed a row-incremental version for datasets with billions of data points and up to 10,000 dimensions. By adding a Tikhonov regularization term and using the Sherman-Morrison-Woodbury formula, we developed a column-incremental LS-SVM to process datasets with a small number of data points but very high dimensionality. Finally, by applying boosting to these incremental LS-SVM algorithms, we developed classification algorithms for massive, very-high-dimensional datasets, and we also applied these ideas to build boosting of other efficient SVM algorithms proposed by Mangasarian, including Lagrange SVM (LSVM), proximal SVM (PSVM) and Newton SVM (NSVM). Numerical test results on UCI, RCV1- binary, Reuters-21578, Forest cover type and KDD cup 1999 datasets showed that our algorithms are often significantly faster and/or more accurate than state-of- the-art algorithms LibSVM, SVM-perf and CB-SVM.

Large Scale Classification with Support Vector Machine Algorithms

In this paper, we present a genetically induced oblique decision tree algorithm. In traditional decision tree, each internal node has a testing criterion involving a single attribute. Oblique decision tree allows testing criterion to consist of more than one attribute. Here we use genetic programming to evolve and find an optimal testing criterion in each internal node for the set of samples at that node. This testing criterion is the characteristic function of a relation over existing attributes. We present the algorithm for construction of the oblique decision tree. We also compare the results of our proposed oblique decision tree with the one of C4.5 algorithm.

Using genetic programming for the induction of oblique decision trees

A representative (but unimodal) problem, the fifteen puzzle, is used to test six different learning systems: some that fit, count, or partition data in instance space; some that optimize measures derived from data in hypothesis space; and some that perform combinations of such procedures. The six systems are described, tested, and analyzed. Through several experiments, specific properties are extracted. By combining two or three kinds of techniques, the extent to which they complement each other is gauged. It is concluded that combinations of strengths can overcome difficulties in domains that are simultaneously probabilistic, dynamic, noisy, and biased. >

Induction as optimization

The need for tools to aid the selection of the CI models that lie at the heart of many AI systems has never been greater, due to the mainstreaming of data mining and other AI applications. LEONARDO -our contribution to this process- is a recommender system that selects and ranks applicable CI models for a given problem based on the peculiarities of the domain as determined by the user's preferences and dataset characteristics. Leonardo's recommendations are based on two knowledge bases. One contains the description of 65 CI models and provides the Meta knowledge for pruning the space of all CI models to only those applicable to the current task. The second KB contains the performance results of over 200 datasets on the applicable CI models. LEONARDO's ranking is achieved by using the performance information of the k entries, from this KB, nearest in similarity to the new domain dataset.

LEONARDO - The computational intelligence (CI) model selection wizard

Pattern Recognition (PR) in its standard form, which involves training and testing classifiers on a representative set of data drawn from a domain of interest, has been applied to automate an immense number of classification tasks. More recently, a challenging set of so-called “one-class” classification problems have been identified and explored. In this thesis, we introduce a further challenging class of PR problems, involving the recognition of Stochastically Episodic (SE) events, and present a first attempt at classifying them within their characteristic fields of background noise. More specifically, this class of problems is characterized by the presence of an overwhelming number of background measurements, which are acquired in the form of a time-series. The time-series is, however, interwoven with a minute number of random (in time, space and magnitude) SE events, which are deemed to be of considerable interest and require classification. The rarity and random nature of the SE events, along with their presence within a time-series of noise-like measurements, renders the learning of their corresponding distribution extremely difficult, if not entirely impossible. By extension, the classification of the SE events is an extremely interesting and ambitious undertaking. Since the acquisition of a sufficient number of SE events is, by definition, unachievable, we propose a flexible framework for the modelling and simulation of such events, as they propagate through a field of background noise. In practice, the initiator of the SE event may take many forms, such as an earthquake, tsunami, erroneous release of pollution into the environment, etc.. The thesis, thereafter, considers the PR of these events from two perspectives; namely those of binary and one-class classification. The thesis contains an empirical demonstration of all these concepts, based on the exemplary scenario that is suggested by the verification of the Comprehensive Test-Ban-Treaty (CTBT).

/pdf/modelling-and-classifying-stochastically-episodic-events-3ordstkmgn.pdf

Modelling and Classifying Stochastically Episodic Events

The effect of data character on empirical concept learning has typically been studied using data from real domains. This presents problems because such data is often limited and uncontrollable. The authors present a more complex approach. They examine typical problems in an effort to characterize concepts more completely. They than utilize the characterization to generate data artificially. From this controlled data, they measure learning performance (speed and accuracy) as a precise function of several data characteristics. The authors' experiments lead to some novel conclusions: a useful starting point to clarify data character is the definition of the term concept, which is effectively a function over instance space; characterizing a concept as a function allows the mimicking of natural data and the control of the generation of artificial data for extensive experimentation; data characteristics are numerous and easy to overlook; and, compared with significant design factors of learning algorithms, certain data characteristics are highly significant. >

The effect of data character on empirical concept learning

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The effect of data character on empirical concept learning