35956760
Analysis of recursive feature elimination methods
Recursive feature elimination using SVM (SVM-RFE) has become an effective approach in selecting predictive features. For example, [1] found 8 genes from 7,000 for cancer prediction using SVM-RFE. It is not well understood, however, whether the success of SVM-RFE comes from the recursive process or the choice of the classifier? And, more generally, what property of a classifier would make it successful in RFE? We answer this question by examining multiple classifiers (SVM, ridge regression (RR) and Rocchio) with feature selection in recursive and non-recursive settings, on two DNA microarray datasets and a text categorization benchmark dataset (Reuters-21578).
10555290
From Lasso regression to Feature vector machine
Lasso regression tends to assign zero weights to most irrelevant or redundant features, and hence is a promising technique for feature selection. Its limitation, however, is that it only offers solutions to linear models. Kernel machines with feature scaling techniques have been studied for feature selection with non-linear models. However, such approaches require to solve hard non-convex optimization problems. This paper proposes a new approach named the Feature Vector Machine (FVM). It reformulates the standard Lasso regression into a form isomorphic to SVM, and this form can be easily extended for feature selection with non-linear models by introducing kernels defined on feature vectors. FVM generates sparse solutions in the nonlinear feature space and it is much more tractable compared to feature scaling kernel machines. Our experiments with FVM on simulated data show encouraging results in identifying the small number of dominating features that are non-linearly correlated to the response, a task the standard Lasso fails to complete.
15260186
Using recursive classification to discover predictive features
Finding most predictive features for statistical classification is a challenging problem and has important applications. Support Vector Machines (SVMs), for example, have been found successful with a recursive procedure in selecting most important genes for cancer prediction. It is not well understood, however, how much the success depends on the choice of the classifier, and how much on the recursive procedure. We answer this question by examining multiple classifiers (SVM, ridge regression and Rocchio) with feature selection in recursive and non-recursive settings, on a DNA microarray dataset (AMLALL) and a text categorization benchmark (Reuters-21578). We found recursive ridge regression most effective: its best classification performance (zero error) on the AMLALL dataset was obtained when using only 3 genes (selected from over 7000), which is more impressive than the best published result on the same benchmark - zero error of recursive SVM using 8 genes. On Reuters-21578, recursive ridge regression also achieves the best result ever published (the improvement was verified in a significance test). An in-depth analysis of the experimental results shows that the choice of classifier heavily influences the recursive feature selection process: the ridge regression classifier tends to penalize redundant features to a much larger extent than the SVM does.
5198300
Compiler-directed channel allocation for saving power in on-chip networks
Increasing complexity in the communication patterns of embedded applications parallelized over multiple processing units makes it difficult to continue using the traditional bus-based on-chip communication techniques. The main contribution of this paper is to demonstrate the importance of compiler technology in reducing power consumption of applications designed for emerging multi processor, NoC (Network-on-Chip) based embedded systems. Specifically, we propose and evaluate a compiler-directed approach to NoC power management in the context of array-intensive applications, used frequently in embedded image/video processing. The unique characteristic of the compiler-based approach proposed in this paper is that it increases the idle periods of communication channels by reusing the same set of channels for as many communication messages as possible. The unused channels in this case take better advantage of the underlying power saving mechanism employed by the network architecture. However, this channel reuse optimization should be applied with care as it can hurt performance if two or more simultaneous communications are mapped onto the same set of channels. Therefore, the problem addressed in this paper is one of reducing the number of channels used to implement a set of communications without increasing the communication latency significantly. To test the effectiveness of our approach, we implemented it within an optimizing compiler and performed experiments using twelve application codes and a network simulation environment. Our experiments show that the proposed compiler-based approach is very successful in practice and works well under both hardware based and software based channel turn-off schemes.
3002704
Approximate aggregation techniques for sensor databases
In the emerging area of sensor-based systems, a significant challenge is to develop scalable, fault-tolerant methods to extract useful information from the data the sensors collect. An approach to this data management problem is the use of sensor database systems, exemplified by TinyDB and Cougar, which allow users to perform aggregation queries such as MIN, COUNT and AVG on a sensor network. Due to power and range constraints, centralized approaches are generally impractical, so most systems use in-network aggregation to reduce network traffic. However, these aggregation strategies become bandwidth-intensive when combined with the fault-tolerant, multipath routing methods often used in these environments. For example, duplicate-sensitive aggregates such as SUM cannot be computed exactly using substantially less bandwidth than explicit enumeration. To avoid this expense, we investigate the use of approximate in-network aggregation using small sketches. Our contributions are as follows: 1) we generalize well known duplicate-insensitive sketches for approximating COUNT to handle SUM, 2) we present and analyze methods for using sketches to produce accurate results with low communication and computation overhead, and 3) we present an extensive experimental validation of our methods.
1037934
A visual tool for building logical data models of websites
Information sources over the WWW contain a large amount of data organized according to different interests and values. Thus, it is important that facilities are there to enable users to extract information of interest in a simple and effective manner. To do this, We propose the Wiccap Data Model, an XML data model that maps Web information sources into commonly perceived logical models, so that information can be extracted automatically according to users' interests. To accelerate the creation of data models, we have implemented a visual tool, called the Mapping Wizard, to facilitate and automate the process of producing Wiccap Data Models. Using the tool, the time required to construct a logical data model for a given website is significantly reduced.
11193384
Compiler-directed voltage scaling on communication links for reducing power consumption
Reducing power consumption of communication networks is an important optimization goal in many application domains, ranging from large-scale simulation codes to embedded multi-media applications. Most of the prior efforts on network power optimization are hardware-based schemes. These schemes are predictive by definition as they control communication link status based on the observations made In the past. Since prediction may not be very accurate most of the time, these approaches can result in overheads in terms of both performance and power. This paper proposes a compiler driven approach to communication link voltage management. In this approach, an optimizing compiler analyzes the application code and extracts the data communication pattern among parallel processors. This information along with network topology is used for identifying the link access patterns. These patterns and the inherent data dependence information of the underlying code help the compiler decide the optimum voltages/frequencies to be used for communication links at a given time frame. Our focus in this work is on loop-intensive codes which frequently appear in data intensive video and image processing. We exploit the regularity in data accesses of these codes to abstract out their inter-processor communication patterns, which In turn enable us select the most appropriate voltage/frequency level to employ for each communication link at any time.
15048202
Protection and guarantee for voice and video traffic in IEEE 802.11e wireless LANs
In order to support multimedia applications such as voice and video over the wireless medium, a contention-based channel access function, called enhanced distributed coordination function (EDCF), is being developed in the emerging standard IEEE 802.11e. In EDCF, differentiated services are provided for different traffic classes. In this paper, we propose a two-level protection and guarantee mechanism for voice and video traffic in IEEE 802.11e wireless LANs. In the first-level protection, the existing voice and video flows are protected from the new and other existing voice and video flows. In the second-level protection, the voice and video flows are protected from the best-effort data traffic. For each protection level, a couple of protection mechanisms are proposed. Extensive simulation results show that the proposed two-level protection and guarantee mechanism is very effective in terms of protecting and guaranteeing existing voice and video flows as well as fully utilizing the channel capacity