We present the HIClass (Hyper Interactive text Classification) system, an interactive text classification system which combines the cognitive power of humans with the power of automated learners to make statistically sound classification decisions. ...

RISE is a well-known multi-strategy learning algorithm that combines rule induction and instance-based learning. It achieves higher accuracy than some state-of-the-art learning algorithms, but for large data sets it has a very high average running time. ...

This paper proposes an efficient method to learn from multi source data with an Inductive Logic Programming method. The method is based on two steps. The first one consists in learning rules independently from each source. In the second step the learned ...

In this paper we present a new way of constructing almost phylogenetic trees. Almost since we reconstruct the tree, but without the timestamps. Rather than basing the tree on genetic sequence data ours is based on developmental sequence data. Using ...

One of the most important operations involving Data Mining patterns is computing their similarity. In this paper we present a general framework for comparing both simple and complex patterns, i.e., patterns built up from other patterns. Major features of ...

Attribute noise can affect classification learning. Previous work in handling attribute noise has focused on those predictable attributes that can be predicted by the class and other attributes. However, attributes can often be predictive but ...

In this paper, we propose a new spatial clustering method, called DBRS+, which aims to cluster spatial data in the presence of both obstacles and facilitators. It can handle datasets with intersected obstacles and facilitators. Without preprocessing, ...

Automated tools for knowledge discovery are frequently invoked in databases where objects already group into some known classification scheme. In the context of unsupervised learning or clustering, such tools delve inside large databases looking for ...

Spam filtering is a text categorization task that has attracted significant attention due to the increasingly huge amounts of junk email on the Internet. While current best-practice systems use Naive Bayes filtering and other probabilistic methods, we ...

Clustering results could be comprehensible and usable if individual groups are associated with characteristic descriptions. However, characterization of clusters followed by clustering may not always produce clusters associated with special features, ...

Feature selection techniques are often used to reduce data dimensionality, increase classification performance, and gain insight into the processes that generated the data. In this paper, we describe an iterative procedure of feature selection and ...

The paper deals with the problem of knowledge discovery in spatial databases. In particular, we explore the application of decision tree learning methods to the classification of spatial datasets. Spatial datasets, according to the Geographic Information ...

Besides good predictive performance, the naive Bayesian classifier can also offer a valuable insight into the structure of the training data and effects of the attributes on the class probabilities. This structure may be effectively revealed through ...

Episode rules are patterns that can be extracted from a large event sequence, to suggest to experts possible dependencies among occurrences of event types. The corresponding mining approaches have been designed to find rules under a temporal constraint ...

The purpose of this study is to propose a new online and nonlinear PCA(OL-NPCA) method for feature extraction from the incremental data. Kernel PCA(KPCA) is widely used for nonlinear feature extraction, however, it has been pointed out that KPCA has the ...

A crucial issue for Machine Learning and Data Mining is Feature Selection, selecting the relevant features in order to focus the learning search. A relaxed setting for Feature Selection is known as Feature Ranking, ranking the features with respect to ...

Protein fingerprints are groups of conserved motifs which can be used as diagnostic signatures to identify and characterize collections of protein sequences. These fingerprints are stored in the prints database after time-consuming annotation by domain ...

Effective incorporation of human expertise, while exerting a low cognitive load, is a critical aspect of real-life text classification applications that is not adequately addressed by batch-supervised high-accuracy learners. Standard text classifiers ...

Many real-world machine learning tasks are faced with the problem of small training sets. Additionally, the class distribution of the training set often does not match the target distribution. In this paper we compare the performance of many learning ...

A calibrated classifier provides reliable estimates of the true probability that each test sample is a member of the class of interest. This is crucial in decision making tasks. Procedures for calibration have already been studied in weather forecasting,...