Intelligent Data Analysis: An International Journal

Learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We derive a cost-sensitive perceptron learning rule for non-separable classes, that can be extended to multi-modal classes (DIPOL) and present a natural cost-sensitive extension of the support vector machine (SVM). We also derive an approach for including example dependent costs into an arbitrary cost-insensitive learning algorithm by sampling according to modified probability distributions.

When applying an unordered set of classification rules, the rules may assign more than one class to a particular example. Previous methods of resolving such conflicts between rules include using the most frequent class of the examples covered by the conflicting rules (as done in CN2) and using naïve Bayes to calculate the most probable class. An alternative way of solving this problem is presented in this paper: by generating new rules from the examples covered by the conflicting rules. These newly induced rules are then used for classification. Experiments on a number of domains show that this method significantly outperforms both the CN2 approach and naïve Bayes.

Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any model that is learnt from the data will average over the different dependency structures. Paradigms that try to explain underlying processes and observed events in multivariate time series must explicitly model these changes in order to allow non-experts to analyse and understand such data. In this paper we have developed a method for generating explanations in multivariate time series that takes into account changing dependency structure. We make use of a dynamic Bayesian network model with hidden nodes. We introduce a representation and search technique for learning such models from data and test it on synthetic time series and real-world data from an oil refinery, both of which contain changing underlying structure. We compare our method to an existing EM-based method for learning structure. Results are very promising for our method and we include sample explanations, generated from models learnt from the refinery dataset.

Many individuals, organizations, and companies have to answer large amounts of emails. Often, many of these emails contain variations of relatively few frequently asked questions. We address the problem of predicting which of several frequently used answers a user will choose to respond to an email. We map the problem to a semi-supervised text classification problem. In a case study with emails that have been sent to a corporate customer service department, we investigate the ability of the naive Bayesian and support vector classifier to identify the appropriate answers to emails. We study how effectively the transductive Support Vector Machine and the co-training algorithm utilize unlabeled data and investigate why co-training is only beneficial when very few labeled data are available. In addition, we describe a practical assistance system.

The main task of drug discovery is to find novel bioactive molecules, i.e., chemical compounds that, for example, protect human cells against a virus. One way to support solving this task is to analyze a database of known and tested molecules with the aim to build a classifier that predicts whether a novel molecule will be active or inactive, so that future chemical tests can be focused on the most promising candidates. In [1] an algorithm for constructing such a classifier was proposed that uses molecular fragments to discriminate between active and inactive molecules. In this paper we present two extensions of this approach: A special treatment of rings and a method that finds fragments with wildcards based on chemical expert knowledge.

A broad range of mathematical techniques, ranging from statistics to fuzzy logic, have been used to great advantage in intelligent data analysis. Topology – the fundamental mathematics of shape – has to date been conspicuously absent from this repertoire. This paper shows how topology, properly reformulated for a finite-precision world, can be useful in intelligent data analysis tasks.