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1. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
MULTILABEL CLASSIFICATION VIA CO-EVOLUTIONARY MULTILABEL
HYPERNETWORK
ABSTRACT:
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 computational 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.

2. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
CONCLUSIONS
In this paper, we propose a Co-MLHN method to deal with multilabel
classification problems. We firstly convert the conventional hypernetwork into
a multilabel hyper-network. We then propose a co-evolutionary learning
algorithm to learn a multilabel hypernetwork from train-ing data. Experimental
results on a variety of multilabel data sets show that Co-MLHN is effective in
addressing multilabel classification problems and scales well with respect to
the number of labels. In Co-MLHN, we only consider the inclusive correla-tions
among labels, i.e. the co-occurrence of labels. How-ever, the exclusive
relationship among labels is also im-portant to multilabel classification. In the
future, we will study how to exploit both inclusive and exclusive correla-tions
among labels using hypernetwork..
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3. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
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4. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
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