We present an improved inductive learning method to derive classification rules that correctly describe most of the examples belonging to a class and do not describe most of the examples not belonging to this class. The problem is represented as a modification of the set covering problems solved by a genetic algorithm. Its is employed to medical data on coronary disease, and the results seem to...

Constrained based inductive systems are a key component of inductive databases and responsible for building the models that satisfy the constraints in the inductive queries. In this paper, we propose a constraint based system for building multi-objective regression trees. A multi-objective regression tree is a decision tree capable of predicting several numeric variables at once. We focus on si...

Both intensional and extensional background knowledge have previously been used in inductive problems to complement the training set used for a task. In this research, we propose to explore the usefulness, for inductive learning, of a new kind of intensional background knowledge: the inter-relationships or conditional probability distributions between subsets of attributes. Such information cou...

A method for inductive learning of primitive features is proposed. Primitive features are well investigated and they are extracted in bottom-up manner fiom training set of patterns by a segmentation method based on genetic algorithm. Merging is applied to selected primitive candidates to filter out their redundancy as similarity and inclusion. Experimental results with map figures are shown.

Data mining is an instance of the inductive methodology. Many philosophical considerations for induction can also be carried out for data mining. In particular, the justification of induction has been a long-standing problem in epistemology. This article is a recast of the problem in the context of data mining. We formulate the problem precisely in the rough set-based decision logic and discuss...

In 2010, Haiti was struck by the worst natural disaster in 200 years. The work of first responders was helped by micro-blogging services and crisis mapping systems that were deployed for rescue missions. These systems provided the capability to reshape the crisis response by facilitating collective response through citizen reports, visualization and interactive mapping by (1) enabling crisis in...

Testing a black-box system without recourse to a specification is difficult, because there is no basis for estimating how many tests will be required, or to assess how complete a given test set is. Several researchers have noted that there is a duality between these testing problems and the problem of inductive inference (learning a model of a hidden system from a given set of examples). It is ...

In inductive inference, a machine is given words of a language (a recursively enumerable set in our setting) and the machine is said to identify the language if it correctly names the language. In this paper we study identifiability of classes of languages where the unions of up to a fixed number (n say) of languages from the class are provided as input. We distinguish between two different sce...

We present an inductive learning algorithm that allows for a partial completeness and consistence, i.e. that derives classification rules correctly describing, e.g, most of the examples belonging to a class and not describing most of the examples not belonging to this class. The problem is represented as a modification of the set covering problem that is solved by a greedy algorithm. The approa...

A common theme in symbolic model checking is to compute an inductive strengthening of the desired invariant, which forms a proof that no erroneous state can be reached by the system. The original symbolic model checking algorithm computed this inductive strengthening by computing (a hopefully succinct) representation of all reachable states by fixpoint computations and OBDDs. This set of reacha...