By François Rioult, Bruno Zanuttini, Bruno Crémilleux (auth.), Zbigniew W. Ras, Li-Shiang Tsay (eds.)
Intelligent info platforms (IIS) should be outlined because the subsequent new release of knowledge structures (IS) constructed because of integration of AI and database (DB) applied sciences. IIS include wisdom that enables them to express clever habit, lets them cooperate with clients and different platforms in challenge fixing, discovery, retrieval, and manipulation of information and information. For any IIS to serve its function, the data needs to be on hand whilst it's wanted. which means the computing structures used to shop facts and approach the data, and the safety controls used to guard it needs to be functioning appropriately. This publication covers the various above themes and it's divided into 4 sections: category, Approximation and knowledge defense, wisdom administration, and alertness of IIS to clinical and song domains.
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Extra info for Advances in Intelligent Information Systems
P(n|s)) an entropy function such as Shannon’s entropy for instance Hn (p1 , p2 , . . , pn ) = − ∑ni=1 pi log2 pi . Asymmetric and Sample Size Sensitive Entropy Measures for Supervised Learning 31 There are many other entropy measures   such as the quadratic entropy Hn (p1 , p2 , . . , pn ) = ∑ni=1 pi (1 − pi ) for instance. The Figure 1 depicts the quadratic and Shannon entropies for 2 classes. All the pictures of entropy measures have the same shape. 2 Fig. 3 Properties of the Entropy Measures Let’s suppose that (p1 , p2 , .
N n n Strict concavity: The function h(p1 , p2 , . . , pn ) is strictly concave. 1 Asymmetric Criteria The properties of classical entropy measures such as those cited above (Shannon, quadratic) are not suited to inductive learning for many reasons : • First, the uniform distribution is not necessarily the most uncertain. e. account for the sample size. That is why we proposed in  a new axiomatic leading to a new family of more general measures. They make it possible for the user to define a reference distribution that is viewed as of maximal entropy.
The rules of type 1) and 3) represent certain knowledge extracted from the decision table, while the rules of type 2) and 4) represent possible knowledge. Rules of type 5) represent doubtful knowledge. 1 Example Illustrating DRSA in the Context of Ordinal Classiﬁcation This subsection presents a didactic example which illustrates the main concepts of DRSA. Let us consider the following ordinal classiﬁcation problem. Students of a college must obtain an overall evaluation on the basis of their achievements in Mathematics, Physics and Literature.