21,470 research outputs found
A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition
Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high
prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—oversampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates
Scaling properties of protein family phylogenies
One of the classical questions in evolutionary biology is how evolutionary
processes are coupled at the gene and species level. With this motivation, we
compare the topological properties (mainly the depth scaling, as a
characterization of balance) of a large set of protein phylogenies with a set
of species phylogenies. The comparative analysis shows that both sets of
phylogenies share remarkably similar scaling behavior, suggesting the
universality of branching rules and of the evolutionary processes that drive
biological diversification from gene to species level. In order to explain such
generality, we propose a simple model which allows us to estimate the
proportion of evolvability/robustness needed to approximate the scaling
behavior observed in the phylogenies, highlighting the relevance of the
robustness of a biological system (species or protein) in the scaling
properties of the phylogenetic trees. Thus, the rules that govern the
incapability of a biological system to diversify are equally relevant both at
the gene and at the species level.Comment: Replaced with final published versio
Understanding Search Trees via Statistical Physics
We study the random m-ary search tree model (where m stands for the number of
branches of a search tree), an important problem for data storage in computer
science, using a variety of statistical physics techniques that allow us to
obtain exact asymptotic results. In particular, we show that the probability
distributions of extreme observables associated with a random search tree such
as the height and the balanced height of a tree have a traveling front
structure. In addition, the variance of the number of nodes needed to store a
data string of a given size N is shown to undergo a striking phase transition
at a critical value of the branching ratio m_c=26. We identify the mechanism of
this phase transition, show that it is generic and occurs in various other
problems as well. New results are obtained when each element of the data string
is a D-dimensional vector. We show that this problem also has a phase
transition at a critical dimension, D_c= \pi/\sin^{-1}(1/\sqrt{8})=8.69363...Comment: 11 pages, 8 .eps figures included. Invited contribution to
STATPHYS-22 held at Bangalore (India) in July 2004. To appear in the
proceedings of STATPHYS-2
- …