Interactive Inductive Learning System

Inductive learning system learns classification from training examples and uses induced rules for classifying new instances. If a decision cannot be inferred from system rule base, a default rule is usually applied. No approaches with human interaction exist that would provide model of interactivity appropriate for dealing with non-classifiable instances. In the paper a new interactive approach is proposed where in uncertain conditions interactive inductive learning system can ask for human decision and improve its knowledge base with the rule derived from this decision. Problems and solutions of incorporation of human-made decision into rule base and aspects of choosing between static and incremental learning algorithms are analyzed in the context of proposed approach

Interactive Inductive Learning System: The Proposal

Inductive learning system learns classification from training examples and uses induced rules for classifying new instances. If a decision cannot be inferred from system rule base, a default rule is usually applied. No approaches with human interaction exist that would provide model of interactivity appropriate for dealing with non-classifiable instances. In the paper a new interactive approach is proposed where in uncertain conditions interactive inductive learning system can ask for human decision and improve its knowledge base with the rule derived from this decision. Problems and solutions of incorporation of human-made decision into rule base and aspects of choosing between static and incremental learning algorithms are analyzed in the context of proposed approach

Machine Learning Based Study Course Comparison

Student mobility and curriculum development have created the need to compare study programmes (curricula) and study courses in order to analyze their compatibility. This paper presents an approach for machine learning based study course comparison. It is based on relevant information extraction from course descriptions and interactive inductive learning system which uses extracted information to build the classifier for course compatibility analysis

Interactive Inductive Learning Based Study Course Comparison

Globalization and student mobility have led to the need for study programme and study course comparison in order to analyze their compatibility. This paper discusses use of inductive learning in the area of curricula management. Presented approach for indirect study course comparison tends to teach the classifier the assessment system of human expert in order to compare unseen study courses semi-automatically. Therefore human involvement in comparison tasks minimizes over the time as the classifier is getting smarter. Unlike classical inductive learning methods that apply a default rule if the classifier cannot classify a course, interactive inductive learning system can ask for human advice thus improving its performance

An Environmental Impact Assessment Learning Apprentice System

In engineering problems with a weak domain theory, such as environmental evaluation, machine learning techniques should bring to bear any existing background knowledge so as to guide the knowledge acquisition process. This paper proposes an Interactive Inductive Learning System (IILS) which can use background knowledge provided by the experts to avoid many incorrect or incomplete induced heuristics. Through the specification of guidance relations, the expert can force the rule induction system to focus on a subset of relevant attributes and training instances. A guidance relation consists of a set of attribute constraints, where each constraint instructs IILS on the role of an attribute for the induction of a concept. IILS was linked to the database of an Environmental Evaluation Support System (EESS) for the induction of impact estimation and comparison heuristics. The induced heuristics are generalizations of past assessments stored in the database and permit the prediction of impac..