2310778
Boosting an associative classifier
Associative classification is a new classification approach integrating association mining and classification. It becomes a significant tool for knowledge discovery and data mining. However, high-order association mining is time consuming when the number of attributes becomes large. The recent development of the AdaBoost algorithm indicates that boosting simple rules could often achieve better classification results than the use of complex rules. In view of this, we apply the AdaBoost algorithm to an associative classification system for both learning time reduction and accuracy improvement. In addition to exploring many advantages of the boosted associative classification system, this paper also proposes a new weighting strategy for voting multiple classifiers.
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.
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.
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.
22289409
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
15440093
Pattern discovery: a data driven approach to decision support
Decision support nowadays is more and more targeted to large scale complicated systems and domains. The success of a decision support system relies mainly on its capability of processing large amounts of data and efficiently extracting useful knowledge from the data, especially knowledge which is previously unknown to the decision makers. With a large scale system, traditional knowledge acquisition models become inefficient and/or more biased, due to the subjectivity of the experts or the pre-assumptions of certain ideas or algorithmic procedures. Today, with the rapid development of computer technologies, the capability of collecting data has been greatly advanced. Data becomes the most valuable resource for an organization. We present a fundamental framework toward intelligent decision support by analyzing a large amount of mixed-mode data (data with a mixture of continuous and categorical values) in order to bridge the subjectivity and objectivity of a decision support process. By considering significant associations of artifacts (events) inherent in the data as patterns, we define patterns as statistically significant associations among feature values represented by joint events or hypercells in the feature space. We then present an algorithm which automatically discovers statistically significant hypercells (patterns) based on: 1) a residual analysis, which tests the significance of the deviation when the occurrence of a hypercell differs from its expectation, and 2) an optimization formulation to enable recursive discovery. By discovering patterns from data sets based on such an objective measure, the nature of the problem domain will be revealed. The patterns can then be applied to solve specific problems as being interpreted or inferred with.
40469721
Modeling, Learning and Simulating Biological Cells with Entity Grammar
In recent years, whole cell modeling approaches, which combine existing knowledge and machine learning results, have received considerable attention. These approaches are potentially very efficient for simulating and analyzing physiological function of cells. In this work, entity grammar system is proposed as formalism for knowledge representation and multistratgy learning techniques in systems biology. Modeling biological cells with entity grammar starts from the simple grammatical models. Integrating the simple models into a cooperating entity grammar of cell facilitates real-time model learning and updating. This makes a difference with many other formalisms to build preset models. The scheme of such a platform is described in the paper and the possible applications are discussed. The proposed formalism method is open to all reasoning paradigm and can be used for studying biological complex systems.
20961254
Strictly Nonblocking Multirate logd(N, m, p) Networks
We give necessary and sufficient conditions for the d-nary multilog network to be strictly nonblocking under the discrete multirate model, and we give sufficient conditions for the same under the continuous multirate model.
38555873
Recognizing Human Action from Videos Using Histograms of Visual Words
We propose a new method for human action recognition from video sequences using histograms of visual words. Video sequences are represented by a novel "bag-of-words" representation, where each frame corresponds to a "word". The major difference between our model and previous "bag-of-words" models for recognition problems in computer vision is that, a "word" in our representation corresponds to a whole frame. The advantage of this representation is that the large-scale feature of a frame is better captured, which turns out to be important for recognition actions. We demonstrate our approach on two publicly available datasets. Our results are comparable to other state-of-the-art approaches for action recognition.
9251959
Visual Zen art: aesthetic cognitive dissonance in Japanese dry stone garden measured in visual PageRank
One of the most famous Japanese landscape of dry stone Zen gardens in Ryoan temple established AC 1450 at Kyoto by the deputy Shogun Katsumoto Hosokawa, a UNESCO World Heritage Site, has the small 248 m2 empty rectangle where white sand was laid in between, in the abstract, placements of 15 rocks with mosses that seem to be scattered in seemingly haphazard. The landscape attracts many visitors, including Queen Elizabeth II, 1975, to hundreds of thousands for long years. The stone placements designed by the ancient anonymous landscape designer look random at a glance, however, the placement structure is hidden in white sand of the garden space in sophisticated manner, and that we are in perfect harmony with the temple buildings and landscapes. The stone structure looks puzzling at glance. However, the general stone placement structure can immediately be revealed by understanding the ancient garden design rule texts described in, for example, “Sakuteiki” (“Landscape Design” in Japanese, published around AC 1200). According to this instruction text, the stones are placed recursively and in fractal as an obtuse inequilateral triangle in different three level scales. The three stones are placed as vertices entitled “真 (very formal)” at the obtuse angle, “副(formal)” at the vertex closest to “very formal”, and “対(Casual)” at the remaining vertex. These three stones form a cluster and three clusters form a different obtuse inequilateral triangle, recursively, as shown in Image (lower). If we plot the size and the rank of the three level obtuse triangles in log scale, we obtain the perfect Zipf’s law[Gabaix 1999] or 1/f pink noise[Voss 1985]. This means that the stones are placed in a perfect fractal manner, and this fractal stone placement is completely understood by the garden viewers unconsciously in our eyetracking measurements shown in Image (upper). Eye tracking experiments are performed and the visual “PageRank”[Page et al. 1998] of eye movement are measured. We measured 10 testers’ eye movement while they were watching the landscape garden. All testers spend more than 90% of their time to watch the stone objects, and move their eyes from a stone vertex to another, following the triangles, recursively. Image (upper) shows a typical eye movement trajectory in 20 seconds, and the trajectory follows the three level of fractal triangle structure displayed in Image (lower). Five human figures 1 to 5 in different colors represent different viewing positions corresponding to different eye movement trajectories in their colors. We assume that the eye movement from one stone or one cluster to another is a forward “link”. Using the same technique as “PageRank”, the eye movement from one stone to another is taken as a directed graph. First, we generate an adjacency matrix of this graph structure with weighted sum of the number of eye visits. Next, we obtain the largest absolute eigenvalue of this matrix and its eigenvector. Third, the normalized component values of the eigenvector are the visual “PageRanks”, and now we rank the stones or the cluster in order. The visual “PageRank” in the Japanese dry stone Zen garden reveals the amazing hidden structure in this old landscape garden, which cannot be discovered by a simple hot spot diagram that only represents the length of fixation time. Figure 1 indicates that the “PageRanks” in two “真 (very formal)” stones (number 2-2 and 3-2) are significantly higher than the others, where the general fractal placement manner is somehow violated. At these stones circled in red in Image, the eye trajectories are strongly disturbed and some psychological tension caused by a disparity between what one expects to see and what one actually sees is observed. This is a well-known phenomenon in social psychology today called “cognitive dissonance” or “visual dissonance” although totally not known in 560 years ago when the temple was build. The cognitive dissonance happens when we perceive a discrepancy between our attitudes and our behaviors. Our eye sees the world of art with a thousand of expectations based on our personalities and our cognitive structure or knowledge system. Sometimes those expectations are fulfilled, sometimes not. In the case of unfulfilled expectations, the viewer is required to resolve his or her tension, or simply abandon the piece and consider another. An important part of human motivation is found in “dissonance reduction”, in that people do not normally choose to live in a state of psychological tension. In psychological terms, it is better to be avoided or resolved such an aversive state. The technique to produce unexpected visual forms is widely practiced by modern artists who seek to gain our attentions. However, the most striking feature here is that this cognitive dissonance was implemented in a very naive way and viewer observe this only unconsciously, not like many visual dissonance in modern arts. When people view this garden people feels deep serenity and perfect harmony with very small “disharmony” i. e. “dissonance” like in much beautiful music.
3104032
Robot Navigation by Waypoints
In this paper we propose a novel waypoint-based robot navigation method that combines reactive and deliberative actions. The approach uses reactive exploration to generate waypoints that can then be used by a deliberative system to plan future movements through the same environment. The waypoints are used largely to provide the interface between reactive and deliberative navigation and a range of methods could be used for either type of navigation. In the current work, an incremental decision tree method is used to navigate the robot reactively from the specified initial position to its destination avoiding obstacles in its path and a genetic algorithm method is used to perform the deliberative navigation. The new method is shown to have a number of practical advantages. Firstly, in contrast with many deliberative approaches, complete knowledge of the environment is not required, nor is it necessary to make assumptions regarding the geometry of obstacles. Secondly, the presence of a reactive navigator means it is always possible to continue directed movements in unknown or changing environments or when time constraints become particularly demanding. Thirdly, the use of waypoints allows escape from certain obstacle configurations that would normally trap robots navigated under the control of purely reactive methods. In addition, the results presented in this paper from a number of realistic simulated environments show that the adoption of waypoints significantly reduces the time to calculate a deliberative path.