Fundamenta Informaticae - Volume 59, issue 2-3

Authors: Nguyen, Tuan Trung

Article Type: Research Article

Abstract: Classification systems working on large feature spaces, despite extensive learning, often perform poorly on a group of atypical samples. The problem can be dealt with by incorporating domain knowledge about samples being recognized into the learning process. We present a method that allows to perform this task using a rough approximation framework. We show how human expert's domain knowledge expressed in natural language can be approximately translated by a machine learning recognition system. We present in …details how the method performs on a system recognizing handwritten digits from a large digit database. Our approach is an extension of ideas developed in the rough mereology theory. Show more

Keywords: rough mereology, concept approximation, domain knowledge, machine learning, handwritten digit recognition

Citation: Fundamenta Informaticae, vol. 59, no. 2-3, pp. 261-270, 2004

Authors: Tsumoto, Shusaku

Article Type: Research Article

Abstract: One of the most important problems with rule induction methods is that they cannot extract rules, which plausibly represent expert decision processes. In this paper, the characteristics of experts' rules are closely examined and a new approach to extract plausible rules is introduced, which consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the concept hierarchy for given classes is calculated. Second, based on the hierarchy, …rules for each hierarchical level are induced from data. Then, for each given class, rules for all the hierarchical levels are integrated into one rule. The proposed method was evaluated on a medical database, the experimental results of which show that induced rules correctly represent experts' decision processes. Show more

Keywords: rule induction, grouping, coverage, rough sets, granular computing

Citation: Fundamenta Informaticae, vol. 59, no. 2-3, pp. 271-285, 2004

Authors: Zhang, Ling | Zhang, Bo

Article Type: Research Article

Abstract: The paper introduces a framework of quotient space theory of problem solving. In the theory, a problem (or problem space) is represented as a triplet, including the universe, its structure and attributes. The problem spaces with different grain sizes can be represented by a set of quotient spaces. Given a problem, the construction of its quotient spaces is discussed. Based on the model, the computational complexity of hierarchical problem solving and the information combination are also …dealt with. The model can also be extended to the fuzzy granular world. Show more

Keywords: quotient space, problem solving, granular computing, hierarchy

Citation: Fundamenta Informaticae, vol. 59, no. 2-3, pp. 287-298, 2004

Authors: Zheng, Zheng | Wang, Guoyin

Article Type: Research Article

Abstract: As a special way in which the human brain is learning new knowledge, incremental learning is an important topic in AI. It is an object of many AI researchers to find an algorithm that can learn new knowledge quickly, based on original knowledge learned before, and in such way that the knowledge it acquires is efficient in real use. In this paper, we develop a rough set and rule tree based incremental knowledge acquisition algorithm. It can learn from …a domain data set incrementally. Our simulation results show that our algorithm can learn more quickly than classical rough set based knowledge acquisition algorithms, and the performance of knowledge learned by our algorithm can be the same as or even better than classical rough set based knowledge acquisition algorithms. Besides, the simulation results also show that our algorithm outperforms ID4 in many aspects. Show more

Keywords: rough set, rule tree, incremental learning, knowledge acquisition, data mining

Citation: Fundamenta Informaticae, vol. 59, no. 2-3, pp. 299-313, 2004