6,907 research outputs found
Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches
The ICDM Challenge 2013 is to apply machine learning to the problem of hotel
ranking, aiming to maximize purchases according to given hotel characteristics,
location attractiveness of hotels, user's aggregated purchase history and
competitive online travel agency information for each potential hotel choice.
This paper describes the solution of team "binghsu & MLRush & BrickMover". We
conduct simple feature engineering work and train different models by each
individual team member. Afterwards, we use listwise ensemble method to combine
each model's output. Besides describing effective model and features, we will
discuss about the lessons we learned while using deep learning in this
competition.Comment: 6 pages, 3 figure
Regression and Learning to Rank Aggregation for User Engagement Evaluation
User engagement refers to the amount of interaction an instance (e.g., tweet,
news, and forum post) achieves. Ranking the items in social media websites
based on the amount of user participation in them, can be used in different
applications, such as recommender systems. In this paper, we consider a tweet
containing a rating for a movie as an instance and focus on ranking the
instances of each user based on their engagement, i.e., the total number of
retweets and favorites it will gain.
For this task, we define several features which can be extracted from the
meta-data of each tweet. The features are partitioned into three categories:
user-based, movie-based, and tweet-based. We show that in order to obtain good
results, features from all categories should be considered. We exploit
regression and learning to rank methods to rank the tweets and propose to
aggregate the results of regression and learning to rank methods to achieve
better performance. We have run our experiments on an extended version of
MovieTweeting dataset provided by ACM RecSys Challenge 2014. The results show
that learning to rank approach outperforms most of the regression models and
the combination can improve the performance significantly.Comment: In Proceedings of the 2014 ACM Recommender Systems Challenge,
RecSysChallenge '1
A proposal project for a blind image quality assessment by learning distortions from the full reference image quality assessments
This short paper presents a perspective plan to build a null reference image
quality assessment. Its main goal is to deliver both the objective score and
the distortion map for a given distorted image without the knowledge of its
reference image.Comment: International Workshop on Quality of Multimedia Experience, 2012,
Melbourne, Australi
Some variations on Ensembled Random Survival Forest with application to Cancer Research
In this paper we describe a novel implementation of adaboost for prediction
of survival function. We take different variations of the algorithm and compare
the algorithms based on system run time and root mean square error. Our
construction includes right censoring data and competing risk data too. We take
different data set to illustrate the performance of the algorithms.Comment: 16 pages; 10 figure
Scalable Multilabel Prediction via Randomized Methods
Modeling the dependence between outputs is a fundamental challenge in
multilabel classification. In this work we show that a generic regularized
nonlinearity mapping independent predictions to joint predictions is sufficient
to achieve state-of-the-art performance on a variety of benchmark problems.
Crucially, we compute the joint predictions without ever obtaining any
independent predictions, while incorporating low-rank and smoothness
regularization. We achieve this by leveraging randomized algorithms for matrix
decomposition and kernel approximation. Furthermore, our techniques are
applicable to the multiclass setting. We apply our method to a variety of
multiclass and multilabel data sets, obtaining state-of-the-art results
Non-uniform Feature Sampling for Decision Tree Ensembles
We study the effectiveness of non-uniform randomized feature selection in
decision tree classification. We experimentally evaluate two feature selection
methodologies, based on information extracted from the provided dataset:
\emph{leverage scores-based} and \emph{norm-based} feature selection.
Experimental evaluation of the proposed feature selection techniques indicate
that such approaches might be more effective compared to naive uniform feature
selection and moreover having comparable performance to the random forest
algorithm [3]Comment: 7 pages, 7 figures, 1 tabl
Comparing various regression methods on ensemble strategies in differential evolution
Differential evolution possesses a multitude of various strategies for
generating new trial solutions. Unfortunately, the best strategy is not known
in advance. Moreover, this strategy usually depends on the problem to be
solved. This paper suggests using various regression methods (like random
forest, extremely randomized trees, gradient boosting, decision trees, and a
generalized linear model) on ensemble strategies in differential evolution
algorithm by predicting the best differential evolution strategy during the
run. Comparing the preliminary results of this algorithm by optimizing a suite
of five well-known functions from literature, it was shown that using the
random forest regression method substantially outperformed the results of the
other regression methods
Predicting the Behavior of the Supreme Court of the United States: A General Approach
Building upon developments in theoretical and applied machine learning, as
well as the efforts of various scholars including Guimera and Sales-Pardo
(2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model
designed to predict the voting behavior of the Supreme Court of the United
States. Using the extremely randomized tree method first proposed in Geurts, et
al. (2006), a method similar to the random forest approach developed in Breiman
(2001), as well as novel feature engineering, we predict more than sixty years
of decisions by the Supreme Court of the United States (1953-2013). Using only
data available prior to the date of decision, our model correctly identifies
69.7% of the Court's overall affirm and reverse decisions and correctly
forecasts 70.9% of the votes of individual justices across 7,700 cases and more
than 68,000 justice votes. Our performance is consistent with the general level
of prediction offered by prior scholars. However, our model is distinctive as
it is the first robust, generalized, and fully predictive model of Supreme
Court voting behavior offered to date. Our model predicts six decades of
behavior of thirty Justices appointed by thirteen Presidents. With a more sound
methodological foundation, our results represent a major advance for the
science of quantitative legal prediction and portend a range of other potential
applications, such as those described in Katz (2013).Comment: 17 pages, 6 figures; source available at
https://github.com/mjbommar/scotus-predic
Consistency of Random Survival Forests
We prove uniform consistency of Random Survival Forests (RSF), a newly
introduced forest ensemble learner for analysis of right-censored survival
data. Consistency is proven under general splitting rules, bootstrapping, and
random selection of variables--that is, under true implementation of the
methodology. A key assumption made is that all variables are factors. Although
this assumes that the feature space has finite cardinality, in practice the
space can be a extremely large--indeed, current computational procedures do not
properly deal with this setting. An indirect consequence of this work is the
introduction of new computational methodology for dealing with factors with
unlimited number of labels.Comment: Submitted to the Electronic Journal of Statistics
(http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics
(http://www.imstat.org
Randomized Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) is a powerful tool for data mining.
However, the emergence of `big data' has severely challenged our ability to
compute this fundamental decomposition using deterministic algorithms. This
paper presents a randomized hierarchical alternating least squares (HALS)
algorithm to compute the NMF. By deriving a smaller matrix from the nonnegative
input data, a more efficient nonnegative decomposition can be computed. Our
algorithm scales to big data applications while attaining a near-optimal
factorization. The proposed algorithm is evaluated using synthetic and real
world data and shows substantial speedups compared to deterministic HALS.Comment: This is an extended and revised version of the paper which appeared
in JPR
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