We propose a novel classification learning method called customized support pattern learner (CSPL). Given an instance to be classified, CSPL explores and discovers support patterns (SPs), which are essentially attribute value subsets of the instance to be classified. The final prediction of the class label is performed by combining some statistics of the discovered useful SPs. One advantage of the CSPL method is that it can explore a richer hypothesis space and discover useful classification patterns involving attribute values with almost indistinguishable information gain. The customized learning characteristic also allows that the target class can vary for different instances to be classified. It facilitates extremely easy training instance maintenance and updates. We have evaluated our method with real-world problems and benchmark data sets. The results demonstrate that CSPL can achieve good performance and high reliability.

We propose a novel lazy learning method called QPAL. QPAL does not simply utilize a kind of distance measure between the query instance and training instances as many lazy learning methods do. It attempts to discover useful patterns known as query projections, which are customized to the query instance. The discovery for useful QPs is conducted in an innovative way. QPAL can guarantee to discover high-quality QPs in the learning process. We use some benchmark data sets and a spam email filtering problem to evaluate QPAL and demonstrate that QPAL achieves good performance and high reliability.

We propose a query-driven lazy learning algorithm which attempts to discover useful local patterns, called support patterns, for classifying a given query. The learning is customized to the query to avoid the horizon effect. We show that this query-driven learning algorithm can guarantee to discover all support patterns with perfect expected accuracy in polynomial time. The experimental results on benchmark data sets also demonstrate that our learning algorithm really has prominent learning performance.

SMILE (Scanning Memory Image LatticE) is a lazy learning framework based on a memory image lattice scanning technique. To classify an unseen instance, the instances in the training set will generate a memory image lattice in terms of the similarities between the training instances and the unseen instance. An exploration algorithm of memory image lattice is designed to search an appropriate set of images of training instances to produce the final prediction. SMILE differs from other lazy learning algorithms in that it utilizes subsets of attribute values as much as possible. This design leads to a more flexible model which is less sensitive to data sparseness.

We propose a framework which can exploit hierarchical structures of feature domain values to improve classification performance. Mean-variance analysis method under this framework is investigated. One characteristic of our framework is that it provides a principled way to transform an original feature domain value to a coarser granularity by utilizing the underlying hierarchical structure. Through this transformation, a tradeoff between precision and robustness is achieved to improve the parameter estimation in classification learning. We have conducted an experiment using a biological data set and. The results demonstrate that utilizing domain value hierarchies gains benefits for classification.

We propose a new approach to text categorization known as generalized instance set (GIS) algorithm under the framework of generalized instance patterns. Our GIS algorithm unifies the strengths of k-NN and linear classifiers and adapts to characteristics of text categorization problems. It focuses on refining the original instances and constructs a set of generalized instances. We also propose a metamodel framework based on category feature characteristics. It has a metalearning phase which discovers a relationship between category feature characteristics and each component algorithm. Extensive experiments have been conducted on two large-scale document corpora for both GIS and the metamodel. The results demonstrate that both approaches generally achieve promising text categorization performance.