Multilabel classification is prevalent in many real-world applications where data instances may be associated with multiple labels simultaneously. In multilabel classification, exploiting label correlations is an essential but nontrivial task. Most of the existing multilabel learning algorithms are either ineffective or computationally demanding and less scalable in exploiting label correlations. In this paper, we propose a co-evolutionary multilabel hypernetwork (Co-MLHN) as an attempt to exploit label correlations in an effective and efficient way. To this end, we firstly convert the traditional hypernetwork into a multilabel hypernetwork (MLHN) where label correlations are explicitly represented. We then propose a co-evolutionary learning algorithm to learn an integrated classification model for all labels. The proposed Co-MLHN exploits arbitrary order label correlations and has linear computational complexity with respect to the number of labels. Empirical studies on a broad range of multilabel data sets demonstrate that Co-MLHN achieves competitive results against state-of-the-art multilabel learning algorithms, in terms of both classification performance and scalability with respect to the number of labels.

Multi-label classification has attracted significant attentions in machine learning. In multi-label classification, exploiting correlations among labels is an essential but nontrivial task. First, labels may be correlated in various degrees. Second, the scalability may suffer from the large number of labels, because the number of combinations among labels grows exponentially as the number of labels increases. In this paper, a multi-label hypernetwork (MLHN) is proposed to deal with these problems. By extending the traditional hypernetwork model, MLHN can represent arbitrary order correlations among labels. The classification model of MLHN is simple and the computational complexity of MLHN is linear with respect to the number of labels, which contribute to the good scalability of MLHN. We perform experiments on a variety of datasets. The results illustrate that the proposed MLHN achieves competitive performances against state-of-the-art multi-label classification algorithms in terms of both effectiveness and scalability with respect to the number of labels.

A marionette is a puppet operated by human manipulator using strings and control bar. It is an ancient and universal form of performing art. Marionettes have been used for entertainment for centuries because people are fascinated with unique and funny movements of marionettes.

This paper presents a novel partially reconfigurable FIR filter design that employs dynamic partial reconfiguration. Our scope is to implement a low-power, area-efficient autonomously reconfigurable digital signal processing architecture that is tailored for the realization of arbitrary response FIR filters. The implementation of design addresses area efficiency and flexibility allowing dynamically inserting and/or removing the partial modules. This design method shows the configuration time improvement by small configured slice and good area efficiency as compared to the method of conventional FIR filters.

In this paper, a genetic algorithm-based adaptive image enhancement filtering scheme is proposed and implemented on an FPGA board. In contrast to conventional filter systems, the proposed system can find an optimal combination of filters, as well as their sequent order and parameter values, adaptively under unknown noise types using structured genetic algorithms. For evaluation, three types of noise were used, and the experimental results showed that the proposed scheme can generate an optimal set of filters adaptively without a-priori noise information.