DAC: Discriminative Associative Classification

Associative explanations of discriminative inhibition effects

Classification by Discriminative Regularization

Classification is one of the most fundamental problems in machine learning, which aims to separate the data from different classes as far away as possible. A common way to get a good classification function is to minimize its empirical prediction loss or structural loss. In this paper, we point out that we can also enhance the discriminality of those classifiers by further incorporating the dis...

Lazy Associative Graph Classification

In this paper, we introduce a modification of the lazy associative classification which addresses the graph classification problem. To deal with intersections of large graphs, graph intersections are approximated with all common subgraphs up to a fixed size similarly to what is done with graphlet kernels. We illustrate the algorithm with a toy example and describe our experiments with a predict...

Associative Classification with Knn

Associative classification usually generates a large set of rules. Therefore, it is inevitable that an instance matches several rules which classes are conflicted. In this paper, a new framework called Associative Classification with KNN (AC-KNN) is proposed, which uses an improved KNN algorithm to address rule conflicts. Traditional K-Nearest Neighbor (KNN) is low efficient due to its calculat...

Competence-conscious associative classification

The classification performance of an associative classifier is strongly dependent on the statistic measure or metric that is used to quantify the strength of the association between features and classes (i.e., confidence, correlation etc.). Previous studies have shown that classifiers produced by different metrics may provide conflicting predictions, and that the best metric to use is data-depe...