6,208 research outputs found
Forward stagewise regression and the monotone lasso
We consider the least angle regression and forward stagewise algorithms for
solving penalized least squares regression problems. In Efron, Hastie,
Johnstone & Tibshirani (2004) it is proved that the least angle regression
algorithm, with a small modification, solves the lasso regression problem. Here
we give an analogous result for incremental forward stagewise regression,
showing that it solves a version of the lasso problem that enforces
monotonicity. One consequence of this is as follows: while lasso makes optimal
progress in terms of reducing the residual sum-of-squares per unit increase in
-norm of the coefficient , forward stage-wise is optimal per unit
arc-length traveled along the coefficient path. We also study a condition
under which the coefficient paths of the lasso are monotone, and hence the
different algorithms coincide. Finally, we compare the lasso and forward
stagewise procedures in a simulation study involving a large number of
correlated predictors.Comment: Published at http://dx.doi.org/10.1214/07-EJS004 in the Electronic
Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A NOVEL SPLIT SELECTION OF A LOGISTIC REGRESSION TREE FOR THE CLASSIFICATION OF DATA WITH HETEROGENEOUS SUBGROUPS
A logistic regression tree (LRT) is a hybrid machine learning method that combines a decision tree model and logistic regression models. An LRT recursively partitions the input data space through splitting and learns multiple logistic regression models optimized for each subpopulation. The split selection is a critical procedure for improving the predictive performance of the LRT. In this paper, we present a novel separability-based split selection method for the construction of an LRT. The separability measure, defined on the feature space of logistic regression models, evaluates the performance of potential child models without fitting, and the optimal split is selected based on the results. Heterogeneous subgroups that have different class-separating patterns can be identified in the split process when they exist in the data. In addition, we compare the performance of our proposed method with the benchmark algorithms through experiments on both synthetic and real-world datasets. The experimental results indicate the effectiveness and generality of our proposed method
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees
Deep Reinforcement Learning (DRL) has achieved impressive success in many
applications. A key component of many DRL models is a neural network
representing a Q function, to estimate the expected cumulative reward following
a state-action pair. The Q function neural network contains a lot of implicit
knowledge about the RL problems, but often remains unexamined and
uninterpreted. To our knowledge, this work develops the first mimic learning
framework for Q functions in DRL. We introduce Linear Model U-trees (LMUTs) to
approximate neural network predictions. An LMUT is learned using a novel
on-line algorithm that is well-suited for an active play setting, where the
mimic learner observes an ongoing interaction between the neural net and the
environment. Empirical evaluation shows that an LMUT mimics a Q function
substantially better than five baseline methods. The transparent tree structure
of an LMUT facilitates understanding the network's learned knowledge by
analyzing feature influence, extracting rules, and highlighting the
super-pixels in image inputs.Comment: This paper is accepted by ECML-PKDD 201
Implicit personalization in driving assistance: State-of-the-art and open issues
In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2
Dynamic Data Mining: Methodology and Algorithms
Supervised data stream mining has become an important and challenging data mining task in modern
organizations. The key challenges are threefold: (1) a possibly infinite number of streaming examples
and time-critical analysis constraints; (2) concept drift; and (3) skewed data distributions.
To address these three challenges, this thesis proposes the novel dynamic data mining (DDM)
methodology by effectively applying supervised ensemble models to data stream mining. DDM can be
loosely defined as categorization-organization-selection of supervised ensemble models. It is inspired
by the idea that although the underlying concepts in a data stream are time-varying, their distinctions
can be identified. Therefore, the models trained on the distinct concepts can be dynamically selected in
order to classify incoming examples of similar concepts.
First, following the general paradigm of DDM, we examine the different concept-drifting stream
mining scenarios and propose corresponding effective and efficient data mining algorithms.
• To address concept drift caused merely by changes of variable distributions, which we term
pseudo concept drift, base models built on categorized streaming data are organized and
selected in line with their corresponding variable distribution characteristics.
• To address concept drift caused by changes of variable and class joint distributions, which we
term true concept drift, an effective data categorization scheme is introduced. A group of
working models is dynamically organized and selected for reacting to the drifting concept.
Secondly, we introduce an integration stream mining framework, enabling the paradigm advocated by
DDM to be widely applicable for other stream mining problems. Therefore, we are able to introduce
easily six effective algorithms for mining data streams with skewed class distributions.
In addition, we also introduce a new ensemble model approach for batch learning, following the same
methodology. Both theoretical and empirical studies demonstrate its effectiveness.
Future work would be targeted at improving the effectiveness and efficiency of the proposed
algorithms. Meantime, we would explore the possibilities of using the integration framework to solve
other open stream mining research problems
The Backbone Method for Ultra-High Dimensional Sparse Machine Learning
We present the backbone method, a generic framework that enables sparse and
interpretable supervised machine learning methods to scale to ultra-high
dimensional problems. We solve sparse regression problems with features
in minutes and features in hours, as well as decision tree problems with
features in minutes.The proposed method operates in two phases: we first
determine the backbone set, consisting of potentially relevant features, by
solving a number of tractable subproblems; then, we solve a reduced problem,
considering only the backbone features. For the sparse regression problem, our
theoretical analysis shows that, under certain assumptions and with high
probability, the backbone set consists of the truly relevant features.
Numerical experiments on both synthetic and real-world datasets demonstrate
that our method outperforms or competes with state-of-the-art methods in
ultra-high dimensional problems, and competes with optimal solutions in
problems where exact methods scale, both in terms of recovering the truly
relevant features and in its out-of-sample predictive performance.Comment: First submission to Machine Learning: 06/2020. Revised: 10/202
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