Showing papers on "Classifier chains published in 2011"

Classifier chains for multi-label classification

Abstract: The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has often been overlooked in the literature due to the perceived inadequacy of not directly modelling label correlations. Most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, and that high predictive performance can be obtained without impeding scalability to large datasets. We exemplify this with a novel classifier chains method that can model label correlations while maintaining acceptable computational complexity. We extend this approach further in an ensemble framework. An extensive empirical evaluation covers a broad range of multi-label datasets with a variety of evaluation metrics. The results illustrate the competitiveness of the chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.

MULAN: A Java Library for Multi-Label Learning

Abstract: MULAN is a Java library for learning from multi-label data. It offers a variety of classification, ranking, thresholding and dimensionality reduction algorithms, as well as algorithms for learning from hierarchically structured labels. In addition, it contains an evaluation framework that calculates a rich variety of performance measures.

Bayesian chain classifiers for multidimensional classification

Abstract: In multidimensional classification the goal is to assign an instance to a set of different classes. This task is normally addressed either by defining a compound class variable with all the possible combinations of classes (label power-set methods, LPMs) or by building independent classifiers for each class (binary-relevance methods, BRMs). However, LPMs do not scale well and BRMs ignore the dependency relations between classes. We introduce a method for chaining binary Bayesian classifiers that combines the strengths of classifier chains and Bayesian networks for multidimensional classification. The method consists of two phases. In the first phase, a Bayesian network (BN) that represents the dependency relations between the class variables is learned from data. In the second phase, several chain classifiers are built, such that the order of the class variables in the chain is consistent with the class BN. At the end we combine the results of the different generated orders. Our method considers the dependencies between class variables and takes advantage of the conditional independence relations to build simplified models. We perform experiments with a chain of naive Bayes classifiers on different benchmark multidimensional datasets and show that our approach outperforms other state-of-the-art methods.

Two Stage Classifier Chain Architecture for efficient pair-wise multi-label learning

Abstract: A common approach for solving multi-label learning problems using problem-transformation methods and dichotomizing classifiers is the pair-wise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming, especially in learning problems with large number of labels. To tackle this problem we propose a Two Stage Classifier Chain Architecture (TSCCA) for efficient pair-wise multi-label learning. Six different real-world datasets were used to evaluate the performance of the TSCCA. The performance of the architecture was compared with six methods for multi-label learning and the results suggest that the TSCCA outperforms the concurrent algorithms in terms of predictive accuracy. In terms of testing speed TSCCA shows better performance comparing to the pair-wise methods for multi-label learning.

Object detection and recognition under out of focus conditions

Abstract: A smart-focusing technique includes identifying an object of interest, such as a face, in a digital image. A focus-generic classifier chain is applied that is trained to match both focused and unfocused faces and/or data from a face tracking module is accepted. Multiple focus- specific classifier chains are applied, including a first chain trained to match substantially out of focus faces, and a second chain trained to match slightly out of focus faces. Focus position is rapidly adjusted using a MEMS component.

Multi-label machine learning algorithms for automated image annotation

Abstract: There are two main purposes for this thesis. Firstly we are trying to improve the multi-label classification techniques and secondly we apply these techniques in automated image annotation field. In machine learning part we examine the Ensemble of Classifier Chains (ECC) algorithm. We modify this algorithm in order to improve the per concept (or per label) performance and improve its performance over Mean Average Precision (MAP) metric. Also we suggest techniques to manipulate the existence of label constraints in a data set. We introduce a post-processing step and we suggest two different techniques to operate the different constraints in the data set. In the second part we focus mainly on the data set that we examine in this work. This dataset is taken out from the photo annotation task of ImageCLEF 2010 contest and we give a short description of it. Then we build models depending on two different kinds of information that we have for every image of the data set, the visual information and the textual information. Another contribution of that work is the suggestion of an ensemble model depending only on different kinds of textual information. An interesting thing to mention is that there is an increasing interest for automated image annotation. Many contests are focused on this field while are already some online applications for image annotation. So it is worth to search and simulate multi-label algorithms in image annotation field in order to see how they perform comparing to other machine learning algorithms.

Rapid auto-focus using classifier chains, mems and/or multiple object focusing

Abstract: A smart-focusing technique includes identifying an object of interest, such as a face, in a digital image. A focus-generic classifier chain is applied that is trained to match both focused and unfocused faces and/or data from a face tracking module is accepted. Multiple focus-specific classifier chains are applied, including a first chain trained to match substantially out of focus faces, and a second chain trained to match slightly out of focus faces. Focus position is rapidly adjusted using a MEMS component.