the association rule into classification can improve the accuracy and obtain some valuable rules and information that cannot be captured by other classification approaches. However, the rule generation procedure is very time-consuming when encountering large data set. Besides, traditional classifier building is organized in several separate phases which may also degrade the efficiency of these ...

Associative classification (AC) is a branch in data mining that utilises association rule discovery methods in classification problems. In this paper, we propose a new training method called Looking at the Class (LC), which can be adapted by any rule-based AC algorithm. Unlike the traditional Classification based on Association rule (CBA) training method, which joins disjoint itemsets regardles...

For constructing compact fuzzy rule-based systems with high classification performance, we have already formulated a rule selection problem. Our rule selection problem has two objectives: to minimize the number of selected fuzzy if-then rules (i.e., to minimize the fuzzy rule base) and to maximize the number of correctly classified patterns (i.e., to maximize the classification performance). In...

This study of supervised categorization shows how different kinds of category representations are influenced by the order in which training examples are presented. We used the well-studied 5-4 category structure of Medin and Schaffer (1978) , which allows transfer of category learning to new stimuli to be discriminated as a function of rule-based or similarity-based category knowledge. In the r...

The automatic design of fuzzy rule-based classification systems based on labeled data is considered. It is recognized that both classification performance and interpretability are of major importance and effort is made to keep the resulting rule bases small and comprehensible. For this purpose, an iterative approach for developing fuzzy classifiers is proposed. The initial model is derived from...

We present and evaluate a set of architectures for conversational dialogue systems, exploring rule-based and statistical classification approaches. In a case study, we show that while a rule-based dialogue policy is capable of high performance if perfect natural language understanding is assumed, a direct classification approach that combines the dialogue policy with NLU has practical advantages.

We present and evaluate a set of architectures for conversational dialogue systems, exploring rule-based and statistical classification approaches. In a case study, we show that while a rule-based dialogue policy is capable of high performance if perfect natural language understanding is assumed, a direct classification approach that combines the dialogue policy with NLU has practical advantages.

Multivariate temporal data are hybrid data. Numeric and categorical data type could be consisted of. Most past researches cannot be operated directly on the multivariate temporal data with both types. Additionally, no useful and readable rules are provided in their methods for advanced classification analysis. We proposed Progressive Temporal Class Rule Miner (PTCR-Miner) algorithm to achieve t...

A Classification Association Rule (CAR), a common type of mined knowledge in Data Mining, describes an implicative co-occurring relationship between a set of binary-valued data-attributes (items) and a pre-defined class, expressed in the form of an “antecedent ⇒ consequent-class” rule. Classification Association Rule Mining (CARM) is a recent Classification Rule Mining (CRM) approach that build...

Packet classification has proved to be an important challenge in network processing. It requires comparing each packet against a database of rules and forwarding the packet according to the highest priority matching rule. Packet classification can be seen as the categorization of incoming packets based on their headers according to specific criteria that examine specific fields within a packet ...