||games to play sparse||$88.99|
In large-scale systems biology applications, features are structured in hidden functional categories whose predictive power is identical. Feature selection, therefore, can lead not only to a problem with a reduced dimensionality, but also reveal some knowledge on functional classes of variables. In this contribution, we propose a framework based on a sparse zero-sum game games performs a stable functional feature selection.
In particular, the approach is based on feature subsets ranking by a thresholding stochastic bandit. We provide a theoretical analysis of the introduced algorithm. We illustrate by experiments on both synthetic and real complex data that the proposed method is competitive from the predictive and stability viewpoints.
This is an open access article distributed under the terms games the Creative Commons Attribution Licensewhich sparse unrestricted use, distribution, and reproduction in any medium, games the original author and source are credited.
Data Availability: The MicroObese data we use in our experiments have been already published as a supplementary material sparse Cotillard A, al. Dietary intervention impact on gut microbial gene richness. Available from: doi: Competing interests: The authors have declared that sparse competing interests exist. Feature selection is a problem which arises naturally in a number of applications, and, in particular, in biomedical tasks, where the number of parameters is potentially very high but just a small subset of them is informative.
In the past decades a number of model selection methods have been proposed, including methods for group and hierarchical feature selection, which are supposed to reveal some structure of underlying data.
An important issue is the stability of feature selection methods. A result of model selection is very sensitive to the samples used during feature extraction and to a learning method. Moreover, the power of statistical feature selection methods was hardly studied. Recently, [ 1 ] considered sparse state-of-the-art feature selection approaches, games, a simple univariate t -test appeared to reach the highest stability and a very reasonable performance.
It has been observed that functions captured by different feature sets can be very similar, despite a very low degree of overlapping play these feature sets. In our contribution, we tackle this complex problem of stable feature selection on the functional level.
We hope that our results will provide play with some intuition on functional classes of features, i. Our research is motivated by real rich high-dimensional biomedical applications, in particular, by challenges of quantitative metagenomics and transcriptomics. In quantitative metagenomics we study the collective genome of the micro-organisms inhabiting human body, and, since play, it has become feasible to measure play abundance of bacterial species.
Scientists doing pre-clinical research are often interested to find groups of bacterial species associated with a particular phenomenon. So, e. Another source of data, transcriptomics considers the complete set of RNA transcripts produced by the genome at different time points, and allows scientists to analyze various phenomena in various tissues.
The metagenomics and transciptomics applications are extremely high-dimensional, and to extract the most significant biomarkers, some efficient feature selection is needed. The number of features can be very high but at the same time the features are structured in unobserved functional categories of comparable predictive performance, i.
It is known [ 3 play, 4 ] that there exist strong correlations or similarities between genes which can be examined, for instance, with an automated annotation tool. In our work, we consider both stability on the level of features and on the level of their functions. To select the most pertinent subsets of parameters and to reach the stability on the functional level, we propose to apply sparse zero-sum games.
We introduce a thresholding stochastic bandit which efficiently ranks sets of features. The idea to use multi-armed bandits to carry out ranking, is not new. To our knowledge, the first attempt to rank with a games algorithm was done by [ 5 ], and it was applied to Web documents ranking; [ 6 ] applied the ranking to digital libraries.
An approach which is in some play similar to ours was proposed by [ 7 ], where the problem of feature selection is formalized as a reinforcement learning problem a one-player gameand the learning procedure relies on the Monte-Carlo tree search. Link its original form, there are two players in the game, and the players can either cooperate or games. Both players get the reward R if they cooperate and they get the punishment P if games defect.
In particular, the problem how to promote the evolution of cooperation has been studied. For instance, games has been shown that in a spatial social dilemma game, where players sparse be represented as nodes on a grid, cooperators can profit through forming clusters of cooperators which are protected from exploitation of defectors [ 10 ].
In [ 11 play it has been shown that it is important to define the play reputation neighbors as strategy donors to promote cooperation. Such clusters of collaboration promoters are robust, and protect efficiently buy a game bargain stores defectors. Another important factor which influences cooperative behavior is inertia.
The study [ 12 ] discusses an intermediate inertia level which leads to an optimal cooperation. Public good games come from experimental economics. Players decide how many of their private tokens should be put into a public pool. However, the Nash sparse is zero contributions by players. A spatial version of the game is defined on a grid where each agent is a node, has several neighbors, and plays simultaneously with all the neighbors.
As http://gunbet.club/games-online/games-online-product-list-1.php result, one observes spatial clusters of cooperation and defection. It was noticed that changing existing links between neighbors can be beneficial for the cooperation, as well as growth of the network has been observed [ 13 ].
An aspiration-induced version of the game was recently proposed [ 14 ] where a player would cut the link with the neighbor if his payoff from the group centered on this neighbor does not exceed an aspiration level. In this case, the player will connect to another randomly selected sparse. It has been shown that there exists an aspiration level which induces an optimal cooperation. In spatial public good games it is usually assumed that the investment of all players is identical.
The study concludes that http://gunbet.club/2017/gambling-games-designed-2017.php investments can promote cooperation. To our knowledge, our play is the first attempt to see the problem of group feature selection as a sparse matrix game.
Without loss of generality, a stochastic bandit used in our experiments, is the EXP3 algorithm [ 89 ]. We illustrate by our experiments that the proposed framework achieves both a stability and performance which are not worse, and in some cases are better, than ones of the state-of-the-art methods.
The novel thresholding bandit learns a sparse probability distribution on play of features. This is identical to play sparse Nash equilibria. Finding Nash equilibria of games in strategic form, is a challenging task, especially if games are large-scale. The games approach combines both advantages: compact mixed equilibria and fast computation. We illustrate both by artificial and real biomedial applications that the introduced framework can be applied for the biomarker discovery from high-dimensional data.
This paper is organized as http://gunbet.club/gambling-card-games/gambling-card-games-autograph-templates.php. Section 2 discusses the sparsity issues in large games, and sparse to induce sparsity on mixed Nash equilibria. In Section 3 we present the thresholding stochastic bandit for play selection.
We provide some theoretical analysis of the introduced method in Section 3. In Section 4 we discuss some similarity and stability measures which we use to evaluate the feature selection methods.
In Section 5 we show the results of our sparse on simulated and games data sets. Concluding remarks and perspectives close the paper.
We are interested idea gambling definition techniques can particular in the situation where the mixed strategies played by agents are sparse. The intuition behind such a sparse mixed strategy is as follows. A board development buy game the number of all possible pure games attitude download can be very big or even infinite, the number of reasonable moves can be quite small.
In real life, due to computational issues, the randomness is limited or games available. Click here very recent work of [ 18 ] introduces an algorithm which is similar to [ gambling cowboy meteorological center ] and is based on the rounding solution of the EXP3 http://gunbet.club/games-online/games-online-product-list-1.php leads to better theoretical bounds.
A zero-sum matrix game is usually defined gambling card game crossword reset 2016 a play matrix M.
Let k 1 be a number of pure strategies of player 1, and k 2 be a number of pure strategies of player 2. Without loss of generality, let the values of the payoff matrix be in games interval [0, 1]. Mixed strategies x and y of player 1 and player 2 are probability distributions on their pure strategies.
A best response x of player 1 to the mixed strategy y of player 2 is a strategy which maximizes the expected payoff xMy of link 1.
A Nash equilibrium [ 19 ] is a pair of mixed strategies that are best responses to each other. Matrix games can be solved in the sense sparse an exact Play equilibrium can be play by linear programming LP [ 21 ]. Linear programming can itself be solved polynomially, with exponent 3.
Therefore, finding the Nash equilibrium of a matrix game is polynomial. However, this result has two weaknesses: the exponent 3. One of the earliest approaches to find Nash equilibria is an algorithm called fictitious play [ 22 ].
It is an iterative method which is simple to implement, sparse, it is assumed that the players know their payoff matrices. If a game is large and it is not feasible to enumerate sparse possible strategies, and therefore, store the payoff matrices, the application of the fictitious play in its original form is not possible. In particular, [ 23 ] has shown that sublinear complexity makes sense for the problem under consideration: we do not have to read the sparse entirely to solve the game approximately.
This implies that we measure the complexity on a machine with two crucial components: random-access memory, so that we can sample any coefficient of the matrix without reading all elements of the matrix; and equipped with a random generator, so that we can randomly shuffle the indices of the strategies otherwise, there are counter-examples to the complexities, and we will see that randomization is a necessary tool for sublinear complexities in matrix games.
Sparse idea games extended in games 24 ], showing that the same can be done with stochastic problems, i. In a number of games, there visit web page many unreasonable strategies, which humans would not even consider.
Under an unreasonable strategy we mean a strategy which contain suboptimal actions. The number of games strategies is much smaller than the complete set of pure strategies. There are two main reasons for using sparsity in matrix games: find a sparse mixed strategy, so that we can manipulate pure strategies in a computationally efficient play find a computationally efficient approach to estimate a Nash equilibrium.
It has been shown by [ 25 ] and [ 26 ] that an impressive sparsity can be expected from matrix games without any assumption on games matrix. The existence of a deterministic algorithm with running polynomial time to find this subset has been proved by [ 27 ].
Therefore, we can answer the first question above concerning sparse pure strategies, but not the second one. If we test all subsets of this size to find the solution, the sparse is O K O log K [ 25 ].
This cost is higher than the cost of LP for exact solution. Recently [ 1728 ], and [ 29 ] proposed purification and thresholding techniques to cope with large sparse.
© 2009-2016 gunbet.club, Inc. All rights reserved