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disadvantages. Some of its applications in Predictive Data Mining are discussed in
[6]. Its modular architecture comes with the advantage of incorporating training data
as connectionist structures and human expertise in form of fuzzy rules. The approach
demonstrates better robustness because of the modular combinations [5] of various
incorporated expert opinions. However, one of the encountered challenges is the
significance of its parameters to the quality of the global model.
The next sections will be focused on the formalism proposed to describe the pa-
rameterized structure of HIS and the synergy derived from the use of its complemen-
tary components (Section 2). A formal description of HIS is proposed in Section 3,
together with considerations on the universe of discourse and some issues on integra-
tion algorithms for the development of the global structure. Some implications and
significance of parameters to the system will be further illustrated through a case
study. The application, described in Section 4, covers the use of structural, learning
and descriptive parameters of various knowledge models to tune an integrated system.
A particular case study from predictive toxicology is presented, along with some
preliminary experimental results on the influence of the main parameters of the pro-
posed intelligent system based on the modular integration of implicit and explicit
knowledge modules. In the last section, the advantages of using modular HIS to de-
velop knowledge fusion models and list some potential further research directions are
summarized.
2 Knowledge Representation
The last ten years have produced a tremendous amount of research on fuzzy logic and
connectionist fields. The current directions of research explore high-level connection-
ism and hybrid intelligent systems [2], [7]. The two approaches can be used in a com-
plementary way, HIS combining connectionist and symbolic features. In such sys-
tems, the learner can insert fuzzy rules into neural networks. Once the domain knowl-
edge has a neural representation, training examples are used to refine initial knowl-
edge or additional structures. Finally, it processes the output for given instances and,
using specific methods [8]-[10], can extract symbolic information from trained net-
works, to explain and interpret the refined connectionist knowledge.
The implicit knowledge is defined as connectionist representation of learning data.
An explicit knowledge module has the role to adjust performances of implicit knowl-
edge modules by using external information provided by experts, in form of Fuzzy
Rule-based Systems. In our approach, connectionist integration of explicit and im-
plicit knowledge appears a natural solution to develop homogeneous intelligent sys-
tems. Explicit and implicit rules are represented using MLP (Multi-Layer Perceptron)
[11], neuro-fuzzy [12], fuzzy (FNN) or hybrid (HNN) neural nets [13]. Thus, fuzzy
logic provides the inference mechanism under cognitive uncertainty, since neural nets
offer advantages of learning, adaptation, fault-tolerance, parallelism and generaliza-
tion.
The hybrid intelligent system considered in this paper is a multi-input single-
output (MISO) neuro-fuzzy system (Fig. 1). The general goal is to model a combina-
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