Category: Uncategorized

Clustering issues are fundamental to exploratory analysis of bioinformatics data. This process may follow algorithms that
are reproducible but make assumptions about, for instance, the ability to estimate the global structure by successful local
agglomeration or alternatively, they use pattern recognition methods that are sensitive to the initial conditions. This paper
reviews two clustering methodologies and highlights the differences that result from the changes in data representation, applied
to a protein expression data set for breast cancer (n = 1,076). The two clustering methodologies are a reproducible approach to model-free clustering and a probabilistic competitive
neural network. The results from the two methods are compared with existing studies of the same data set, and the preferred
clustering solutions are profiled for clinical interpretation.

Genomic data, and more generally biomedical data, are often characterized by high dimensionality. An input selection procedure
can attain the two objectives of highlighting the relevant variables (genes) and possibly improving classification results.
In this paper, we propose a wrapper approach to gene selection in classification of gene expression data using simulated annealing
along with supervised classification. The proposed approach can perform global combinatorial searches through the space of
all possible input subsets, can handle cases with numerical, categorical or mixed inputs, and is able to find (sub-)optimal
subsets of inputs giving low classification errors. The method has been tested on publicly available bioinformatics data sets
using support vector machines and on a mixed type data set using classification trees. We also propose some heuristics able
to speed up the convergence. The experimental results highlight the ability of the method to select minimal sets of relevant
features.

As an important approach to solving complex sequential decision problems, reinforcement learning (RL) has been widely studied
in the community of artificial intelligence and machine learning. However, the generalization ability of RL is still an open
problem and it is difficult for existing RL algorithms to solve Markov decision problems (MDPs) with both continuous state
and action spaces. In this paper, a novel RL approach with fast policy search and adaptive basis function selection, which
is called Continuous-action Approximate Policy Iteration (CAPI), is proposed for RL in MDPs with both continuous state and
action spaces. In CAPI, based on the value functions estimated by temporal-difference learning, a fast policy search technique
is suggested to search for optimal actions in continuous spaces, which is computationally efficient and easy to implement.
To improve the generalization ability and learning efficiency of CAPI, two adaptive basis function selection methods are developed
so that sparse approximation of value functions can be obtained efficiently both for linear function approximators and kernel
machines. Simulation results on benchmark learning control tasks with continuous state and action spaces show that the proposed
approach not only can converge to a near-optimal policy in a few iterations but also can obtain comparable or even better
performance than Sarsa-learning, and previous approximate policy iteration methods such as LSPI and KLSPI.

In a previous paper, we generalized to the mixed strategy case the γ model of coalition formation (introduced by Hart and
Kurz in Econometrica 51(4):1047–1064, 1983) for situations in which players have ambiguous expectations about the formation of the coalitions in which they are not
involved; then we analyzed the corresponding evolutionary games. In this paper, we embody into the model rationality of the
players; it follows that allowing for mixed strategies makes it impossible to construct unequivocally a von Neumann–Morgestein
expected utility function coherent (in the sense of de Finetti B in Sul Significato Soggettivo della Probabilità, Fundamenta Mathematicae, T, vol XVIII, pp
298–329, 1931) to every strategy profile. We find out that if the multiplicity of coherent beliefs problem is approached by considering
“ambiguity loving” players then existence results for classical static equilibria can be obtained in this model. Moreover,
we provide conditions for the game to be dynamically playable and we find how the coalition structure beliefs might evolve
coherently (according) to the evolution of the strategies.