20 research outputs found
Kernelized Hashcode Representations for Relation Extraction
Kernel methods have produced state-of-the-art results for a number of NLP
tasks such as relation extraction, but suffer from poor scalability due to the
high cost of computing kernel similarities between natural language structures.
A recently proposed technique, kernelized locality-sensitive hashing (KLSH),
can significantly reduce the computational cost, but is only applicable to
classifiers operating on kNN graphs. Here we propose to use random subspaces of
KLSH codes for efficiently constructing an explicit representation of NLP
structures suitable for general classification methods. Further, we propose an
approach for optimizing the KLSH model for classification problems by
maximizing an approximation of mutual information between the KLSH codes
(feature vectors) and the class labels. We evaluate the proposed approach on
biomedical relation extraction datasets, and observe significant and robust
improvements in accuracy w.r.t. state-of-the-art classifiers, along with
drastic (orders-of-magnitude) speedup compared to conventional kernel methods.Comment: To appear in the proceedings of conference, AAAI-1
Analysis of a Random Forests Model
Random forests are a scheme proposed by Leo Breiman in the 2000's for
building a predictor ensemble with a set of decision trees that grow in
randomly selected subspaces of data. Despite growing interest and practical
use, there has been little exploration of the statistical properties of random
forests, and little is known about the mathematical forces driving the
algorithm. In this paper, we offer an in-depth analysis of a random forests
model suggested by Breiman in \cite{Bre04}, which is very close to the original
algorithm. We show in particular that the procedure is consistent and adapts to
sparsity, in the sense that its rate of convergence depends only on the number
of strong features and not on how many noise variables are present
Risk estimation using probability machines
BACKGROUND: Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. RESULTS: We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. CONCLUSIONS: The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from
Stabilized Nearest Neighbor Classifier and Its Statistical Properties
The stability of statistical analysis is an important indicator for
reproducibility, which is one main principle of scientific method. It entails
that similar statistical conclusions can be reached based on independent
samples from the same underlying population. In this paper, we introduce a
general measure of classification instability (CIS) to quantify the sampling
variability of the prediction made by a classification method. Interestingly,
the asymptotic CIS of any weighted nearest neighbor classifier turns out to be
proportional to the Euclidean norm of its weight vector. Based on this concise
form, we propose a stabilized nearest neighbor (SNN) classifier, which
distinguishes itself from other nearest neighbor classifiers, by taking the
stability into consideration. In theory, we prove that SNN attains the minimax
optimal convergence rate in risk, and a sharp convergence rate in CIS. The
latter rate result is established for general plug-in classifiers under a
low-noise condition. Extensive simulated and real examples demonstrate that SNN
achieves a considerable improvement in CIS over existing nearest neighbor
classifiers, with comparable classification accuracy. We implement the
algorithm in a publicly available R package snn.Comment: 48 Pages, 11 Figures. To Appear in JASA--T&
Classification with imperfect training labels
We study the effect of imperfect training data labels on the performance of classification methods. In a general setting, where the probability that an observation in the training dataset is mislabelled may depend on both the feature vector and the true label, we bound the excess risk of an arbitrary classifier trained with imperfect labels in terms of its excess risk for predicting a noisy label. This reveals conditions under which a classifier trained with imperfect labels remains consistent for classifying uncorrupted test data points. Furthermore, under stronger conditions, we derive detailed asymptotic properties for the popular -nearest neighbour (nn), support vector machine (SVM) and linear discriminant analysis (LDA) classifiers. One consequence of these results is that the nn and SVM classifiers are robust to imperfect training labels, in the sense that the rate of convergence of the excess risks of these classifiers remains unchanged; in fact, our theoretical and empirical results even show that in some cases, imperfect labels may improve the performance of these methods. On the other hand, the LDA classifier is shown to be typically inconsistent in the presence of label noise unless the prior probabilities of each class are equal. Our theoretical results are supported by a simulation study