8955639
An Empirical Study of Building Compact Ensembles
Ensemble methods can achieve excellent performance relying on member classifiers’ accuracy and diversity. We conduct an empirical study of the relationship of ensemble sizes with ensemble accuracy and diversity, respectively. Experiments with benchmark data sets show that it is feasible to keep a small ensemble while maintaining accuracy and diversity similar to those of a full ensemble. We propose a heuristic method that can effectively select member classifiers to form a compact ensemble. The idea of compact ensembles is motivated to use them for effective active learning in tasks of classification of unlabeled data.
40702608
Feature Selection for Clustering
Clustering is an important data mining task. Data mining often concerns large and high-dimensional data but unfortunately most of the clustering algorithms in the literature are sensitive to largeness or high-dimensionality or both. Different features affect clusters differently, some are important for clusters while others may hinder the clustering task. An efficient way of handling it is by selecting a subset of important features. It helps in finding clusters efficiently, understanding the data better and reducing data size for efficient storage, collection and processing. The task of finding original important features for unsupervised data is largely untouched. Traditional feature selection algorithms work only for supervised data where class information is available. For unsupervised data, without class information, often principal components (PCs) are used, but PCs still require all features and they may be difficult to understand. Our approach: first features are ranked according to their importance on clustering and then a subset of important features are selected. For large data we use a scalable method using sampling. Empirical evaluation shows the effectiveness and scalability of our approach for benchmark and synthetic data sets.
8367210
Sensor Sequence Modeling for Driving
Intelligent systems in automobiles need to be aware of the driving and driver context. Available sensor data stream has to be modeled and monitored in order to do so. Currently there exist no building blocks for hierarchical modeling of driving. By semi-supervised segmentation such building blocks can be discovered. We call them drivemes in analogy to phonemes. More parsimonious modeling of driving becomes now possible.
16386059
A Monotonic Measure for Optimal Feature Selection
Feature selection is a problem of choosing a subset of relevant features. In general, only exhaustive search can bring about the optimal subset. With a monotonic measure, exhaustive search can be avoided without sacrificing optimality. Unfortunately, most error- or distancebased measures are not monotonic. A new measure is employed in this work that is monotonic and fast to compute. The search for relevant features according to this measure is guaranteed to be complete but not exhaustive. Experiments are conducted for verification.
16781216
Fast hierarchical clustering and its validation
Clustering is the task of grouping similar objects into clusters. A prominent and useful class of algorithm is hierarchical agglomerative clustering (HAC) which iteratively agglomerates the closest pair until all data points belong to one cluster. It outputs a dendrogram showing all N levels of agglomerations where N is the number of objects in the dataset. However, HAC methods have several drawbacks: (1) high time and memory complexities for clustering, and (2) inefficient and inaccurate cluster validation. In this paper we show that these drawbacks can be alleviated by closely studying the dendrogram. Empirical study shows that most HAC algorithms follow a trend where, except for a number of top levels of the dendrogram, all lower levels agglomerate clusters which are very small in size and close in proximity to other clusters. Methods are proposed that exploit this characteristic to reduce the time and memory complexities significantly and to make validation very efficient and accurate. Analyses and experiments show the effectiveness of the proposed method.
46794790
Analysis of Hidden Representations by Greedy Clustering
The hidden layer of backpropagation neural networks (NNs) holds the key to the networks' success in solving pattern classification problems. The units in the hidden layer encapsulate the network's internal representations of the outside world described by the input data. this paper, the hidden representations of trained networks are investigated by means simple greedy clustering algorithm. This clustering algorithm is applied to networks have been trained to solve well-known problems: the monks problems, the 5-bit problem and the contiguity problem. The results from applying the algorithm to problems with known concepts provide us with a better understanding of NN learning. These also explain why NNs achieve higher predictive accuracy than that of decision-tree methods. The results of this study can be readily applied to rule extraction from Production rules are extracted for the parity and the monks problems, as well as benchmark data set: Pima Indian diabetes diagnosis. The extracted rules from the Indi...
14550653
QoS Topology Control with Minimal Total Energy Cost in Ad Hoc Wireless Networks
This paper discusses the energy efficient QoS topology control problem in ad hoc wireless networks. Given a set of nodes in a plane, end-to-end traffic demands and delay bounds between node pairs, the problem is to find a network topology that can meet the QoS requirements and the total transmission power of nodes is minimized. We consider two cases of the problem: 1) the traffic demands are not splittable, and 2) the traffic demands are splittable. The first case is formulated as an integer linear programming problem. The latter case is formulated as a mixed integer linear programming problem. A greedy algorithm and an approximation algorithm with ratio n are proposed to solve the problem, where n is the number of nodes. Extensive simulations are conducted to evaluate the performance of proposed algorithms.
2465876
Two sufficient conditions for dominating cycles
A cycle C of a graph G is dominating if each component of GnC is edgeless. In the paper, we will give two sufficient conditions for each longest cycle of a 3-connected graph to be a dominating cycle. 2005 Wiley Periodicals, Inc. J Graph Theory 49: 135–150, 2005
15137899
Ensembles of cascading trees
We introduce a new method, called CS4, to construct committees of decision trees for classification. The method considers different top-ranked features as the root nodes of member trees. This idea is particularly suitable for dealing with high-dimensional bio-medical data as top-ranked features in this type of data usually possess similar merits for classification. To make a decision, the committee combines the power of individual trees in a weighted manner. Unlike Bagging or Boosting which uses bootstrapped training data, our method builds all the member trees of a committee using exactly the same set of training data. We have tested these ideas on UCI data sets as well as recent bio-medical data sets of gene expression or proteomic profiles that are usually described by more than 10,000 features. All the experimental results show that our method is efficient and that the classification performance are superior to C4.5 family algorithms.
35256277
Generalized shuffle-exchange digraphs: Hamiltonian properties
The k-ary n-dimensional shuffle-exchange directed network S(k, n) consists of k/sup n/ nodes such that each node is represented as an k-ary n-tuple vector, a/sub 1/a/sub 2/...a/sub n/, where a/sub i/ is in [0, k-1]. Node a/sub 1/a/sub 2/...a/sub n/ is adjacent to node a/sub 2/a/sub 3/...a/sub n/a/sub 1/ (one shuffle link) and k-1 other nodes a/sub 1/a/sub 2/...a/sub n-1/b, where b/spl isin/[0, k-1] and b/spl ne/a/sub n/ (k-1 exchange links). S(k, n) have been widely used as topologies for VLSI networks, parallel architectures, and communication systems. However, since S(k, n) does not exist for the number of nodes between k/sup n/ and k/sup n+1/, Liu and Hsu have recently proposed a class of digraphs GS(k, N), which generalized S(k, n) to any number N of nodes. They also showed that GS(k, N) retains all the nice properties of S(k, n). In this paper, we survey these combinatorial properties and study Hamiltonian properties of GS(k, N). In particular, we have successfully obtained a Hamiltonian circuit for GS(k, k(k+1)) for any k>2.
11306780
Mean-entropy discretized features are effective for classifying high-dimensional biomedical data
This paper studies an empirical feature selection heuristics for classifying high-dimensional bio-medical data. A feature's discriminating power can be measured by its entropy value. Based on this idea, we do not consider those features that are ignored by the entropy idea. Such a selection can usually reduce the dimensionality of the data by 90-95%. Then we rank the remaining features, and select features whose entropy is smaller than the average of all the remaining features' entropies. This round of selection can usually further reduce two thirds of the features. So, we can achieve a reduction from tens of thousands of features to only hundreds of important features. Furthermore, we also observe that learning algorithms, including our new tree-committee classifier, generally improve their accuracy after the feature selection. This heuristics appears to be more systematic than the prevailing use of specific numbers of top-ranked features for classification.