Carin-ALN as proposed recently by Rouveirol and Ventos [2000] is an interesting new rule learning bias for ILP. By allowing description logic terms as predicates of literals in datalog rules, it extends the normal bias used in ILP as it allows the use of all quantified variables in the body of a clause, instead of the normal exist quantified variables and it has atleast and atmost restrictions ...

Discovery of frequent patterns has been studied in a variety of data mining (DM) settings. In its simplest form, known from association rule mining, the task is to nd all frequent itemsets, i.e., to list all combinations of items that are found in a suucient number of examples. A similar task in spirit, but at the opposite end of the complexity scale, is the Inductive Logic Programming (ILP) ap...

This is to present work on modifying the Aleph ILP system so that it evaluates the hypothesised clauses in parallel by distributing the data-set among the nodes of a parallel or distributed machine. The paper briefly discusses the MPI specification and the extension of Yap Prolog with an interface to MPI libraries. It then proceeds to describe an implementation of data-parallel clause evaluatio...

Isolated limb perfusion (ILP) with melphalan and tumor necrosis factor (TNF)-α is used to treat bulky, locally advanced melanoma and sarcoma. However, TNF toxicity suggests a need for better-tolerated drugs. Cilengitide (EMD 121974), a novel cyclic inhibitor of alpha-V integrins, has both anti-angiogenic and direct anti-tumor effects and is a possible alternative to TNF in ILP. In this study, r...

Inductive logic programming (ILP) is a research area which has its roots in inductive machine learning and computational logic. The paper gives an introduction to this area based on a distinction between two diierent semantics used in inductive logic programming, and illustrates their application in knowledge discovery and programming. Whereas most research in inductive logic programming has fo...

Inductive Logic Programming (ILP) is an efficient technique for relational data mining, but when ILP is applied in imperfect domains, the rules induced by ILP often struggle with the overfitting problem. This paper proposes a method to learn first-order Bayesian network (FOBN) which can handle imperfect data powerfully. Due to a high computation cost for directly learning FOBN, we adapt an ILP ...

Inductive logic programming (ILP) is a form of machine learning. The goal ILP to induce hypothesis (a set logical rules) that generalises training examples. As turns 30, we provide new introduction the field. We introduce necessary notation and main learning settings; describe building blocks an system; compare several systems on dimensions; four (Aleph, TILDE, ASPAL, Metagol); highlight key ap...

Inductive Logic Programming (ILP) [9, 11] is the area of AI which deals with the induction of hypothesised predicate definitions from examples and background knowledge. Logic programs are used as a single representation for examples, background knowledge and hypotheses. ILP is differentiated from most other forms of Machine Learning (ML) both by its use of an expressive representation language ...

Cross efficiency evaluation was developed as an extension of DEA. But the traditional DEA models usually have alternative optimal solutions and, as a result, cross efficiency scores may not be unique. It is recommended that without changing the DEA efficiency scores, the secondary goal should be introduced for optimization of the inputs/outputs weights.  Several reports evaluated the perfo...