Feature subset selection: a correlation based filter approach

Recent work has shown that feature subset selection can have a position affect on the performance of machine learning algorithms. Some algorithms can be slowed or their performance adversely affected by too much data some of which may be irrelevant or redundant to the learning task. Feature subset selection, then, is a method of enhancing the performance of learning algorithms, reducing the hypothesis search space, and, in some cases, reducing the storage requirement. This paper describes a feature subset selector that uses a correlation based heuristic to determine the goodness of feature subsets, and evaluates its effectiveness with three common ML algorithms: a decision tree inducer (C4.5), a naive Bayes classifier, and an instance based learner(IBI). Experiments using a number of standard data sets drawn from real and artificial domains are presented. Feature subset selection gave significant improvement for all three algorithms; C4.5 generated smaller decision trees

Practical feature subset selection for machine learning

Machine learning algorithms automatically extract knowledge from machine readable information. Unfortunately, their success is usually dependant on the quality of the data that they operate on. If the data is inadequate, or contains extraneous and irrelevant information, machine learning algorithms may produce less accurate and less understandable results, or may fail to discover anything of use at all. Feature subset selection can result in enhanced performance, a reduced hypothesis search space, and, in some cases, reduced storage requirement. This paper describes a new feature selection algorithm that uses a correlation based heuristic to determine the “goodness” of feature subsets, and evaluates its effectiveness with three common machine learning algorithms. Experiments using a number of standard machine learning data sets are presented. Feature subset selection gave significant improvement for all three algorithm

Factors Influencing College Selection by NCAA Division III Men's Basketball Players

National Collegiate Athletic Association (NCAA) Division III basketball, coaches are often faced with the challenge of stretching resources to successfully recruit players who will represent the institution without the benefit of athletic scholarships. Having a better grasp of the factors that influence the college selection of NCAA Division III players might assist these coaches in more efficiently maximizing their resources. The purposes of this study were to (a) examine specific factors which influence why DIII basketball players made their choice to attend a particular school, (b) determine if there were differences based upon the type of school attended, and (c) examine whether there were categorical factors that differed on the basis of a players recruitment, year in school, race/ethnicity, playing status, or financial aid status.   Using the revised Influential Factors Survey for Student Athletes (IFSSA-R; Pauline, 2010), 503 DIII men’s basketball players were surveyed. Participant responses indicated that ‘career opportunities’ was the most vital item affecting college selection. With regard to the five categorical factors (academic, athletic, coaching staff, financial aid, and social atmosphere) academic factors were also found to be important.   Analyses revealed significant (p < .05) differences as determined by type of school for the athletic, academic, and financial aid subscales.  Results of the study will provide useful information for DIII men’s basketball coaches and college administrators throughout various points in the recruiting and college selection process.  Keywords: coaching, recruiting, basketball, NCAA Division III, college selectio

Towards Interoperability of Biomedical Ontologies

Report on Dagstuhl Seminar 07132, Schloss Dagstuhl, March 27-30 , 2007

The use of graphics in the design of the human-telerobot interface

The Man-Systems Telerobotics Laboratory (MSTL) of NASA's Johnson Space Center employs computer graphics tools in their design and evaluation of the Flight Telerobotic Servicer (FTS) human/telerobot interface on the Shuttle and on the Space Station. It has been determined by the MSTL that the use of computer graphics can promote more expedient and less costly design endeavors. Several specific examples of computer graphics applied to the FTS user interface by the MSTL are described

Guardian of Savannah: Fort McAllister, Georgia, in the Civil War and Beyond

Fort McAllister’s Role in the Civil War Roger S. Durham has examined several aspects of the Civil War along the Georgia coast in his earlier works. In A Confederate Yankee: The Journal of Edward William Drummond, a Confederate Soldier from Maine (2004), he chronicled the experi...

Influence of Primary Electron Beam Angle of Incidence on Auger Electron Spectra

A principal step in the extraction of Auger electron yields and line shapes from electron-excited spectra is an understanding of the attendant electron background. Experiments are reported here in which the influence of the primary electron beam angle of incidence on Auger spectral backgrounds has been separated from that of the angle of electron emission. The secondary electron background for clean polycrystalline palladium, on the high-energy side of the MNN peaks, have been studied as functions of primary beam angle of incidence and beam energy. For the range of beam energies investigated, \u3c 10 keV, linearization of the secondary cascade is easily observed in log-log plots of intensity versus energy. When cascades are fitted with Sickafus-type functions, AE-m (E = electron energy), the parameter m is found to depend on the beam angle of incidence but not on the beam energy. The effect of increasing primary beam energy is to enlarge the energy interval over which linearization can be observed. The low-energy inelastic step or tail accompanying the background-subtracted Auger peaks grows in intensity as the angle of incidence approaches that of the surface normal