Computational Intelligence - November 2015 - 7

Publication
Spotlight

Derong Liu, Chin-Teng Lin,
Kay Chen Tan, Graham Kendall,
and Angelo Cangelosi

CIS Publication Spotlight

IEEE Transactions on Neural
Networks and Learning Systems

A New Method for Data Stream Mining
Based on the Misclassification Error, by
L. Rutkowski, M. Jaworski, L.
Pietruczuk, and P. Duda, IEEE Transactions on Neural Networks and Learning Systems,Vol. 26, No. 5, May 2015,
pp. 1048-1059.
Digital Object Identifier: 10.1109/
TNNLS.2014.2333557
"In this paper, a new method for constructing decision trees for stream data is
developed. First a new splitting criterion
based on the misclassification error is
derived. A theorem is proven showing
that the best attribute computed in considered node according to the available
data sample is the same, with some high
probability, as the attribute derived from
the whole infinite data stream. Next this
result is combined with the splitting criterion based on the Gini index. It is
shown that such combination provides
the highest accuracy among all studied
algorithms."
An Interval Type-2 Neural Fuzzy System for Online System Identification and
Feature Elimination, by C. T. Lin, N. R.
Pal, S. L. Wu, Y. T. Liu, and Y. Y. Lin,
IEEE Transactions on Neural Networks
and Learning Systems, Vol. 26, No. 7,
July 2015, pp. 1442-1455.

Digital Object Identifier 10.1109/MCI.2015.2472117
Date of publication: 13 October 2015

image licensed by creatas

Digital Object Identifier: 10.1109/
TNNLS.2014.2346537
"An integrated mechanism is proposed for discarding derogatory features
and extraction of fuzzy rules based on an
interval type-2 neural fuzzy system
(NFS), which is a more general scheme
that can discard bad features, irrelevant
antecedent clauses, and even irrelevant
rules. High-dimensional input variable
and a large number of rules not only
enhance the computational complexity
of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and
reducing the impact of (or eliminating)
the inferior features is necessary. The
proposed approach, namely an interval
type-2 Neural Fuzzy System for online
System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2
fuzzy sets to model uncertainties associated with information and data in
designing the knowledge base. The consequent part of the IT2NFS-SIFE is of

Takagi-Sugeno-Kang type with interval
weights. The IT2NFS-SIFE possesses a
self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The
antecedent and modulator weights are
learned using a gradient descent algorithm. The consequent part weights are
tuned via the rule-ordered Kalman filter
algorithm to enhance learning effectiveness. Simulation results show that
IT2NFS-SIFE not only simplifies the
system architecture by eliminating
derogatory/irrelevant antecedent clauses,
rules, and features but also maintains
excellent performance."
IEEE Transactions on Fuzzy Systems

Using Data Merging Techniques for
Generating Multidocument Summarizations, by D. Van Britsom, A. Bronselaer, and G. De Tré. IEEE Transactions
on Fuzzy Systems, Vol. 23, No. 3,
June 2015, pp. 576-592.
Digital Object Identifier: 10.1109/
TFUZZ.2014.2317516
"The purpose of this paper is to present that how we can use data merging
techniques to summarize a set of coreferent documents while using soft computing, and specially focused on the fb -optimal merge function, which that uses the
weighted harmonic mean to find a balance between precision and recall. The
fb- optimal merge function is compared
with a distance-based merge function
and several pointwise merge functions

November 2015 | Ieee ComputatIoNal INtellIgeNCe magazINe

Table of Contents for the Digital Edition of Computational Intelligence - November 2015