Instance selection is a pre-processing technique for machine learning and data mining. The main problem is that previous approaches still suffer from the difficulty to produce effective samples for training classifiers. In recent research, a new sampling technique, called Progressive Border Sampling (PBS), has been proposed to produce a small sample from the original labelled training set by id...

This paper aims at characterizing classification problems to find the main features that determine the differences in performance by different classifiers. It is known that, using the disagreements between the classifiers, a distance measure between datasets can be defined. The datasets can then be embedded and visualized in a 2-D scatterplot. This embedding thus reveals the structure of the se...

Error-correcting output codes (ECOC) are a successful technique to combine a set of binary classifiers for multi-class learning problems. However, in traditional ECOC framework, all the base classifiers are trained independently according to the defined ECOC matrix. In this paper, we reformulate the ECOC models from the perspective of multi-task learning, where the binary classifiers are learne...

Logistic regression is presumably the most popular representative of probabilistic discriminative classifiers. In this paper, a kernel variant of logistic regression is introduced as an iteratively re-weighted least-squares algorithm in kernel-induced feature spaces. This formulation allows us to apply highly efficient approximation methods that are capable of dealing with large-scale problems....

Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about Bayesian network classifiers in which we explicitly convert them into Ordered Decision Diagrams (ODDs), which are then used to reason about the properties of these classifiers. Specifically, we present an algorithm for...

Abstract. We study the problem of multiclass classification within the framework of error correcting output codes (ECOC) using margin-based binary classifiers. An important open problem in this context is how to measure the distance between class codewords and the outputs of the classifiers. In this paper we propose a new decoding function that combines the margins through an estimate of their ...

A Bayesian multinet classifier allows a different set of independence assertions among variables in each of a set of local Bayesian networks composing the multinet. The structure of the local network is usually learned using a jointprobability-based score that is less specific to classification, i.e., classifiers based on structures providing high scores are not necessarily accurate. Moreover, ...

Detecting and adapting to concept drift makes learning data stream classifiers a difficult task. It becomes even more complex when the distribution of classes in the stream becomes imbalanced. Currently, proper assessment of classifiers for such data is still a challenge, as existing evaluation measures either do not take into account class imbalance or are unable to indicate class ratio change...

In this paper, a novel batch-mode active learning method based on the nearest average-class distance (ALNACD) is proposed to solve multi-class problems with Linear Discriminate Analysis (LDA) classifiers. Using the Nearest Average-class Distance (NACD) query function, the ALNACD algorithm selects a batch of most uncertain samples from unlabeled data to improve gradually pre-trained classifiers’...

This paper propose two decision fusion-based multitemporal classifiers, namely, the jointly likelihood and the weighted majority fusion classifiers, that are derived using two different definitions of the minimum expected cost. Without any overhead incurred by multitemporal processing, a user-selected conventional pixelwise classifier makes local class decisions separately using each temporal d...