10246554
An Overview of Associative Classifiers
Associative classification is a new classification approach integrating association mining and classification. It becomes a significant tool for knowledge discovery and data mining. This paper presents a critical overview of certain aspects on modelling associative classifiers including event association mining, computational complexity in mining associations, methods of constructing associative classifiers based on the association patterns discovered, some similarities and differences among the reported associative classification systems, and experimental comparisons of these associative classifiers with the conventional classification system such as C4.5.
344886
From Association to Classification: Inference Using Weight of Evidence
Association and classification are two important tasks in data mining and knowledge discovery. Intensive studies have been carried out in both areas. But, how to apply discovered event associations to classification is still seldom found in current publications. Trying to bridge this gap, this paper extends our previous paper on significant event association discovery to classification. We propose to use weight of evidence to evaluate the evidence of a significant event association in support of, or against, a certain class membership. Traditional weight of evidence in information theory is extended here to measure the event associations of different orders with respect to a certain class. After the discovery of significant event associations inherent in a data set, it is easy and efficient to apply the weight of evidence measure for classifying an observation according to any attribute. With this approach, we achieve flexible prediction.
12250963
Boosting for Learning Multiple Classes with Imbalanced Class Distribution
Classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by most standard classifier learning algorithms, which assume a relatively balanced class distribution and equal misclassification costs. This learning difficulty attracts a lot of research interests. Most efforts concentrate on bi-class problems. However, bi-class is not the only scenario where the class imbalance problem prevails. Reported solutions for bi-class applications are not applicable to multi-class problems. In this paper, we develop a cost-sensitive boosting algorithm to improve the classification performance of imbalanced data involving multiple classes. One barrier of applying the cost-sensitive boosting algorithm to the imbalanced data is that the cost matrix is often unavailable for a problem domain. To solve this problem, we apply Genetic Algorithm to search the optimum cost setup of each class. Empirical tests show that the proposed cost-sensitive boosting algorithm improves the classification performances of imbalanced data sets significantly.
2919977
High-Order Pattern Discovery from Discrete-Valued Data
To uncover qualitative and quantitative patterns in a data set is a challenging task for research in the area of machine learning and data analysis. Due to the complexity of real-world data, high-order (polythetic) patterns or event associations, in addition to first-order class-dependent relationships, have to be acquired. Once the patterns of different orders are found, they should be represented in a form appropriate for further analysis and interpretation. The authors propose a novel method to discover qualitative and quantitative patterns (or event associations) inherent in a data set. It uses the adjusted residual analysis in statistics to test the significance of the occurrence of a pattern candidate against its expectation. To avoid exhaustive search of all possible combinations of primary events, techniques of eliminating the impossible pattern candidates are developed. The detected patterns of different orders are then represented in an attributed hypergraph which is lucid for pattern interpretation and analysis. Test results on artificial and real-world data are discussed toward the end of the paper.
16798311
Parameter Inference of Cost-Sensitive Boosting Algorithms
Several cost-sensitive boosting algorithms have been reported as effective methods in dealing with class imbalance problem. Misclassification costs, which reflect the different level of class identification importance, are integrated into the weight update formula of AdaBoost algorithm. Yet, it has been shown that the weight update parameter of AdaBoost is induced so as the training error can be reduced most rapidly. This is the most crucial step of AdaBoost in converting a weak learning algorithm into a strong one. However, most reported cost-sensitive boosting algorithms ignore such a property. In this paper, we come up with three versions of cost-sensitive AdaBoost algorithms where the parameters for sample weight updating are induced. Then, their identification abilities on the small classes are tested on four “real world” medical data sets taken from UCI Machine Learning Database based on F-measure. Our experimental results show that one of our proposed cost-sensitive AdaBoost algorithms is superior in achieving the best identification ability on the small class among all reported cost-sensitive boosting algorithms.
6225988
A global optimal algorithm for class-dependent discretization of continuous data
This paper presents a new method to convert continuous variables into discrete variables for inductive machine learning. The method can be applied to pattern classification problems in machine learning and data mining. The discretization process is formulated as an optimization problem. We first use the normalized mutual information that measures the interdependence between the class labels and the variable to be discretized as the objective function, and then use fractional programming (iterative dynamic programming) to find its optimum. Unlike the majority of class-dependent discretization methods in the literature which only find the local optimum of the objective functions, the proposed method, OCDD, or Optimal Class-Dependent Discretization, finds the global optimum. The experimental results demonstrate that this algorithm is very effective in classification when coupled with popular learning systems such as C4.5 decision trees and Naive-Bayes classifier. It can be used to discretize continuous variables for many existing inductive learning systems.
13286603
Privacy in Online Social Networking at Workplace
Employees using social network sites (SNS) at workplace is a fact. As companies are further embracing social media, how if at all does this practice affect the work dynamics? While privacy has been a hot topic in online social network research in general, there is little work investigating the privacy aspect of this practice at workplace. This paper aims at starting the groundwork towards filling the gap. Based on a review of existing literature in social networks and workplace studies, we hypothesize a number of potential privacy issues in this work practice and suggest future research directions in this area.
2882341
Effective Criterion Functions for Efficient Agglomerative Clustering on Very Large Networks
As the agglomerative clustering algorithm is widely used in data mining, image processing, bioinformatics and pattern recognition. it has attracted great interests from both academical and industrial communities. However, existing studies neglect the decisive factor of the efficiency of the agglomerative clustering algorithm for large complex networks and usually use criterion functions which lead to inefficiency. In this paper, we propose three effective criterion functions for improving performance of agglomerative clustering algorithm. We note that clustering efficiency is determined by two factors: a) the number of neighbors of two merged clusters in each merge step; b) the number of neighbors shared by the two clusters. Based on these observations, we propose a framework for designing criterion functions in order to efficiently find clusters in very large networks. We devise three criterion functions that can effectively control the number of neighbors of clusters, and they can efficiently produce high-quality clusters. We have implemented our method and compared with existing studies on real networks, and our method outperforms state-of-the-art approaches significantly on large networks.
35951417
Tree-based Burst Aggregation in Optical Burst Switching Networks
The invention is a surface light source device capable of effectively producing polarized illumination light. The output angle of a representative beam C3 representing P-polarized component of output light from an emission surface 5 of the light guide plate 1 is about 75 DEG . Where P-polarization component of representative beam C3 is 100 (reference value), and the S-polarized component is 42.2. Representative beam C3 enters from an air layer AR to a flat surface 24g of a prism sheet 24, and then goes into the inside of prism sheet 24. At this stage the P-polarization component is 89.3 whereas the S-polarization component is 25.5. Beam C3 is incident on a slant surface 24d from its inside at an angle proximating the normal, and propagates into air layer AR between prism sheets 24, 34. Beam C3 impinges on a flat surface 34g of a prism sheet 34 while its corresponding part passes through the inside of prism sheet 34. At this stage its P-polarization component is 83.9 and its S-polarization component is 22.3. Beam C3 enters from the inside of one slope 34d of the prism array at the angle of incidence of 31 DEG . Its greater part is output to external air layer AR as illumination light substantially in the front direction.
1200220
Boosting over Groups and Its Application to Acronym-Expansion Extraction
In many real-world classification applications, instances are generated from different `groups'. Take webpage classification as an example, the webpages for training and testing can be naturally grouped by network domains, which often vary a lot from one to another in domain size or webpage template. The differences between `groups' would result that the distribution of instances from different `groups' also vary. Thus, it is not so reasonable to equally treat the instances as the independent elements during training and testing as in conventional classification algorithms. This paper addresses the classification problem where all the instances can be naturally grouped. Specifically, we give a formulation to this kind of problem and propose a simple but effective boosting approach, which is called AdaBoost.Group. The problem is demonstrated by the task of recognizing acronyms and their expansions from text, where all the instances are grouped by sentences. The experimental results show that our approach is more appropriate to this kind of problems than conventional classification approaches.
206733202
Foreground and Shadow Detection Based on Conditional Random Field
This paper presents a conditional random field (CRF) approach to integrate spatial and temporal constraints for moving object detection and cast shadow removal in image sequences. Interactions among both detection (foreground/background/shadow) labels and observed data are unified by a probabilistic framework based on the conditional random field, where the interaction strength can be adaptively adjusted in terms of data similarity of neighboring sites. Experimental results show that the proposed approach effectively fuses contextual dependencies in video sequences and significantly improves the accuracy of object detection.