Intelligent Data Analysis - Volume 10, issue 5

Authors: Kok, Joost N. | Peña, José María | Siebes, Arno

Article Type: Editorial

DOI: 10.3233/IDA-2006-10501

Citation: Intelligent Data Analysis, vol. 10, no. 5, pp. 395-395, 2006

Authors: Fisher, Douglas H. | Edgerton, Mary E. | Chen, Zhihua | Tang, Lianhong | Frey, Lewis

Article Type: Research Article

Abstract: Exploring the vast number of possible feature interactions in domains such as gene expression microarray data is an onerous task. We describe Backward-Chaining Rule Induction (BCRI) as a semi-supervised mechanism for biasing the search for IF-THEN rules that express plausible feature interactions. BCRI adds to a relatively limited tool-chest of hypothesis generation software and is an alternative to purely unsupervised association-rule learning. We illustrate BCRI by using it to search for gene-to-gene causal mechanisms that underlie lung cancer. Mapping hypothesized gene interactions against prior knowledge offers support and explanations for hypothesized interactions, and suggests gaps in current knowledge that induction …might help fill. Our assumption is that “good” hypotheses incrementally extend/revise existing knowledge. BCRI is implemented as a wrapper around a base supervised-rule-learning algorithm. We summarize our prior work with an adaptation of C4.5 as the base algorithm (C45-BCRI), extending this in the current study to use Brute as the base algorithm (Brute-BCRI). In contrast to C4.5's greedy strategy, Brute extensively searches the rule space. Moreover, Brute returns many more rules (i.e., hypothesized feature interactions) than does C4.5. To remain an effective hypothesis-generation tool requires that Brute-BCRI more carefully rank and prune hypothesized interactions than does C45-BCRI. Prior knowledge serves to evaluate final Brute-BCRI rules just as it does with C45-BCRI, but prior knowledge also serves to evaluate and prune intermediate search states, thus maintaining a manageable number of rules for evaluation by a domain expert. Show more

Keywords: Rule induction, hypothesis generation, rule exploration, interactive induction, iterative exploration, machine learning, data mining, prior knowledge, microarray, data analysis, molecular mechanisms, class discovery, semi-supervised learning, decision trees, Brute, non-small cell lung cancer, systems biology

DOI: 10.3233/IDA-2006-10502

Citation: Intelligent Data Analysis, vol. 10, no. 5, pp. 397-417, 2006

Authors: Hüllermeier, Eyke | Beringer, Jürgen

Article Type: Research Article

Abstract: Inducing a classification function from a set of examples in the form of labeled instances is a standard problem in supervised machine learning. In this paper, we are concerned with ambiguous label classification (ALC), an extension of this setting in which several candidate labels may be assigned to a single example. By extending three concrete classification methods to the ALC setting (nearest neighbor classification, decision tree learning, and rule induction) and evaluating their performance on benchmark data sets, we show that appropriately designed learning algorithms can successfully exploit the information contained in ambiguously labeled examples. Our results indicate that the …fundamental idea of the extended methods, namely to disambiguate the label information by means of the inductive bias underlying (heuristic) machine learning methods, works well in practice. Show more

Keywords: Machine learning, classification, missing data, inductive bias, nearest neighbor estimation, decision trees, rule induction

DOI: 10.3233/IDA-2006-10503

Citation: Intelligent Data Analysis, vol. 10, no. 5, pp. 419-439, 2006

Authors: Tucker, Allan | 't Hoen, Peter A.C. | Vinciotti, Veronica | Liu, Xiaohui

Article Type: Research Article

Abstract: The analysis of microarray data from time-series experiments requires specialised algorithms, which take the temporal ordering of the data into account. In this paper we explore a new architecture of Bayesian classifier that can be used to understand how biological mechanisms differ with respect to time. We show that this classifier improves the classification of microarray data and at the same time ensures that the models can easily be analysed by biologists by incorporating time transparently. In this paper we focus on data that has been generated to explore different types of muscular dystrophy.

DOI: 10.3233/IDA-2006-10504

Citation: Intelligent Data Analysis, vol. 10, no. 5, pp. 441-455, 2006

Authors: Pensa, Ruggero G. | Robardet, Céline | Boulicaut, Jean-François

Article Type: Research Article

Abstract: Clustering or co-clustering techniques have been proved useful in many application domains. A weakness of these techniques remains the poor support for grouping characterization. As a result, interpreting clustering results and discovering knowledge from them can be quite hard. We consider potentially large Boolean data sets which record properties of objects and we assume the availability of a bi-partition which has to be characterized by means of a symbolic description. Our generic approach exploits collections of local patterns which satisfy some user-defined constraints in the data, and a measure of the accuracy of a given local pattern as a bi-cluster …characterization pattern. We consider local patterns which are bi-sets, i.e., sets of objects associated to sets of properties. Two concrete examples are formal concepts (i.e., associated closed sets) and the so-called δ-bi-sets (i.e., an extension of formal concepts towards fault-tolerance). We introduce the idea of characterizing query which can be used by experts to support knowledge discovery from bi-partitions thanks to available local patterns. The added-value is illustrated on benchmark data and three real data sets: a medical data set and two gene expression data sets. Show more

Keywords: Co-clustering, characterization, closed sets, fault-tolerant formal concept

DOI: 10.3233/IDA-2006-10505

Citation: Intelligent Data Analysis, vol. 10, no. 5, pp. 457-472, 2006

Authors: Zhang, Zhicheng | Hand, David J.

Article Type: Research Article

Abstract: Pattern discovery is a facet of data mining concerned with the detection of “small local” structures in large data sets. In high dimensions this is typically difficult because of the computational work involved in searching over the data space. In this paper we outline a tool called PEAKER which can detect patterns efficiently in high dimensions. We approach the subject through the two aspects of pattern discovery, detection and verification. We demonstrate various ways of using PEAKER as well as its various inherent properties, emphasizing the exploratory nature of the tool.

Keywords: Pattern discovery, pattern detection, pattern verification, PEAKER

DOI: 10.3233/IDA-2006-10506

Citation: Intelligent Data Analysis, vol. 10, no. 5, pp. 473-483, 2006