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Hiring knowledgeable and cost-effective individuals, who use their knowledge and expertise to boost the organization, is extremely important for organizations as they are the most valuable assets. T-shaped experts are the best option based on agile methodology. The T-shaped professional has a deep understanding of one topic and broad knowledge of several others. Compared to other types of professionals, T-shaped professionals are better communicators and cheaper to hire. Finding T-shaped experts in a given skill area requires determining each candidate's depth of knowledge and shape of expertise. To estimate each candidate's depth of knowledge in a given skill area, we propose a translation-based method that utilizes two attention-based skill translation models to overcome the vocabulary mismatch between skills and user documents. We also propose two new approaches based on binary cross-entropy and focal loss to determine whether each user is T-shaped. Our experiments on three collections of the StackOverflow dataset demonstrate the efficiency of our proposed method compared to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In our digital age, data are generated constantly from public and private sources, social media platforms, and the Internet of Things. A significant portion of this information comes in the form of unstructured images and videos, such as the 95 million daily photos and videos shared on Instagram and the 136 billion images available on Google Images. Despite advances in image processing and analytics, the current state of the art lacks effective methods for discovering, linking, and comprehending image data. Consider, for instance, the images from a crime scene that hold critical information for a police investigation. Currently, no system can interactively generate a comprehensive narrative of events from the incident to the conclusion of the investigation. To address this gap in research, we have conducted a thorough systematic literature review of existing methods, from labeling and captioning to extraction, enrichment, and transforming image data into contextualized information and knowledge. Our review has led us to propose the vision of storytelling with image data, an innovative framework designed to address fundamental challenges in image data comprehension. In particular, we focus on the research problem of understanding image data in general and, specifically, curating, summarizing, linking, and presenting large amounts of image data in a digestible manner to users. In this context, storytelling serves as an appropriate metaphor, as it can capture and depict the narratives and insights locked within the relationships among data stored across different islands. Additionally, a story can be subjective and told from various perspectives, ranging from a highly abstract narrative to a highly detailed one. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of finding the shortest path in stochastic graphs and propose an iterative algorithm for solving it. This algorithm is based on distributed learning automata (DLA), and its objective is to use a DLA for finding the shortest path from the given source node to the given destination node whose weight is minimal in expected sense. At each stage of this algorithm, DLA specifies edges needed to be sampled. We show that the given algorithm finds the shortest path with minimum expected weight in stochastic graphs with high probability which can be close to unity as much as possible. We compare the given algorithm with some distributed learning automata-based iterative algorithms, a particle swarm optimization-based algorithm, an ant colony-based algorithm, a Q-learning-based algorithm, and an actor–critic-based algorithm for finding the shortest path. Computer experiments show that the proposed algorithm requires fewer edge samples to find the shortest path than the previously introduced DLA-based algorithms. [ABSTRACT FROM AUTHOR]

Background: There are many people who are suffering from a variety of physical and mental illnesses due to the chemical attacks. There are various technologies such as recommender systems that can identify the main concerns related to health and make efforts to address them. To design and develop a recommender system, preparation of data source of this system should be considered. The aim of this study was to determine the minimum data set for user profile or user's electronic health record in chemical warfare victims' recommender system. Methods: This applied descriptive, cross-sectional study which was conducted in 2017. A questionnaire was developed by the authors from the data elements that were collected using the data extraction form from the studied sources. Content validity of the questionnaire was confirmed by using the experts. Test–retest method was used to determine the reliability of the questionnaire. The reliability of the questionnaire with Cronbach's alpha coefficient was confirmed as 84%. The questionnaire were submitted for related experts based on Delphi method by email or in person. Data resulting from the Delphi technique with descriptive statistics methods in SPSS software were analyzed. Results: Forty-seven nonclinical data elements and 181 clinical data elements were classified. Conclusion: Determining minimum data set of user profile or electronic health record in the recommender system for chemical warfare victims helps the health authorities to implement the recommender system which demonstrates chemical warfare victims' needs. [ABSTRACT FROM AUTHOR]

We consider a demand response program in which a block of apartments receive a discount from their electricity supplier if they ensure that their aggregate load from air conditioning does not exceed a predetermined threshold. The goal of the participants is to obtain the discount, while ensuring that their individual temperature preferences are also satisfied. As such, the apartments need to collectively optimise their use of air conditioning so as to satisfy these constraints and minimise their costs. Given an optimal cooling profile that secures the discount, the problem that the apartments face then is to divide the total discounted cost in a fair way. To achieve this, we take a coalitional game approach and propose the use of the Shapley value from cooperative game theory, which is the normative payoff division mechanism that offers a unique set of desirable fairness properties. However, applying the Shapley value in this setting presents a novel computational challenge. This is because its calculation requires, as input, the cost of every subset of apartments, which means solving an exponential number of collective optimisations, each of which is a computationally intensive problem. To address this, we propose solving the optimisation problem of each subset suboptimally, to allow for acceptable solutions that require less computation. We show that, due to the linearity property of the Shapley value, if suboptimal costs are used rather than optimal ones, the division of the discount will be fair in the following sense: each apartment is fairly "rewarded" for its contribution to the optimal cost and, at the same time, is fairly "penalised" for its contribution to the discrepancy between the suboptimal and the optimal costs. Importantly, this is achieved without requiring the optimal solutions. [ABSTRACT FROM AUTHOR]

We have entered the era of networked communications where concepts such as big data and social networks are emerging. The explosion and profusion of available data in a broad range of application domains cause data streams to become an inevitable part of the most real-world applications. In the classification of data streams, there are four major challenges: infinite length, concept drift, recurring and evolving concepts. This paper proposes a novel method to address the mentioned challenges with a focus on the last one. Unlike the existing methods for detection of evolving concepts, we cast joint classification and detection of evolving concepts into optimizing an objective function by extending a fuzzy agglomerative clustering method. Moreover, rather than keeping instances or hyper-sphere summaries of previously seen classes, we just maintain boundaries in the kernel space and generate instances of each class on demand. This approach enhances the accuracy and reduces the memory usage of the proposed method. We empirically evaluated and showed the effectiveness of the proposed approach on several synthetic and real datasets. Experimental results on synthetic and real datasets show the superiority of the proposed method over the related state-of-the-art methods in this area. [ABSTRACT FROM AUTHOR]

People use social networks to share millions of stories every day, but these stories rarely become viral. Can we estimate the probability that a story becomes a viral cascade? If so, can we find a set of users that are more likely to trigger viral cascades? These estimation and maximization problems are very challenging since both rare-event nature of viral cascades and efficiency requirement should be considered. Unfortunately, this problem still remains largely unexplored to date. In this paper, given temporal dynamics of a network, we first develop an efficient viral cascade probability estimation method, ViCE, that leverages an special importance sampling approximation to achieve high accuracy, even in the cases of very small probability of influence. We then show that the most influential nodes in this model is NP-hard, and develop an efficient viral cascade probability maximization method, ViCEM, that maximizes a surrogate submodular function using a greedy algorithm. Experiments on both synthetic and real-world data show that ViCE can accurately estimate viral cascade probabilities using fewer samples than naive sampling by at least two orders of magnitude, and also ViCEM finds a set of users with higher viral cascade probability than alternatives. Additionally, experiments show that these algorithms are robust across different network topologies. [ABSTRACT FROM AUTHOR]

Hierarchical reinforcement learning facilitates learning in large and complex domains by exploiting subtasks and creating hierarchical structures using these subtasks. Subtasks are usually defined through finding subgoals of the problem. Providing mechanisms for autonomous subgoal discovery and skill acquisition is a challenging issue in reinforcement learning. Among the proposed algorithms, a few of them are successful both in performance and also efficiency in terms of the running time of the algorithm. In this paper, we study four methods for subgoal discovery which are based on graph partitioning. The idea behind the methods proposed in this paper is that if we partition the transition graph, then the edges connecting two partitions and the end points of these edges are good candidates for subgoals. Hence, we proposed some methods for partitioning the transition graphs and evaluate them on some benchmark problems. The first method uses a genetic algorithm to partition the transition graph. The second method uses graph partitioning learning automata. We provide optimizations to the previous method in the third proposed method. Having observed the features of these methods and their capabilities and drawbacks in subgoal discovery, we propose a novel method, which is based on strongly connected components, to find subgoal states and create skills in early episodes of learning. The proposed method brings together the advantages of the graph partitioning method as well as the experience of graph traversals in earlier episodes. The performance of the proposed method is evaluated by conducting several experiments on famous problems. The results show high accuracy in identifying subgoals that help the learning agent reach its goals expeditiously. [ABSTRACT FROM AUTHOR]

Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods. [ABSTRACT FROM AUTHOR]

Concept drift, change in the underlying distribution that data points come from, is an inevitable phenomenon in data streams. Due to increase in the number of data streams' applications such as network intrusion detection, weather forecasting, and detection of unconventional behavior in financial transactions; numerous researches have recently been conducted in the area of concept drift detection. An ideal method for concept drift detection should be able to rapidly and correctly identify changes in the underlying distribution of data points and adapt its model as quickly as possible while the memory and processing time is limited. In this paper, we propose a novel explicit method based on ensemble classifiers for detecting concept drift. The method processes samples one by one, and monitors the distribution of ensemble's error in order to detect probable drifts. After detection of a drift, a new classifier will be trained on the new concept in order to keep the model up-to-date. The proposed method has been evaluated on some artificial and real benchmark data sets. The experiments' results show that the proposed method is capable of detecting and adjusting to concept drifts from different types, and it has outperformed well-known stateof- the-art methods. Especially, in the case of high-speed concept drifts. [ABSTRACT FROM AUTHOR]

With the increasing volume of data, the best approach for learning from this data is to exploit an online learning algorithm. Online ensemble methods take advantage of an ensemble of classifiers to predict labels of data. Prediction with expert advice is a well-studied problem in the online ensemble learning literature. The weighted majority and the randomized weighted majority (RWM) algorithms are two well-known solutions to this problem, aiming to converge to the best expert. Since among some expert, the best one does not necessarily have the minimum error in all regions of data space, defining specific regions and converging to the best expert in each of these regions will lead to a better result. In this paper, we aim to resolve this problem by proposing a novel online ensemble algorithm to the problem of prediction with expert advice. We propose a cascading version of RWMto achieve not only better experimental results but also a better error bound for sufficiently large datasets. [ABSTRACT FROM AUTHOR]

Multi-agent systems can be seen as an apparatus for testing the performance of real distributed systems. One problem encountered in multi-agent systems with the learning capability is credit assignment. This paper presents two methods for solving this problem. The first method assigns credit to the agents according to the history of the interaction while the second method assigns credit to the agents according to the knowledge of agents, and thus the shares of the agents are extracted from the feedback of the environment. The computer experiments show that critic learning has a positive impact in credit assignment problem. [ABSTRACT FROM AUTHOR]

Recent advances in storage and processing have provided the possibility of automatic gathering of information, which in turn leads to fast and continuous flows of data. The data which are produced and stored in this way are called data streams. Data streams are produced in large size, and much dynamism and have some unique properties which make them applicable to model many real data mining applications. The main challenge of streaming data is the occurrence of concept drift. In addition, regarding the costs of labeling of instances, it is often assumed that only a small fraction of instances are labeled. In this paper, we propose an ensemble algorithm to classify instances of non-stationary data streams in a semi-supervised environment. Furthermore, this method is intended to recognize recurring concept drifts of data streams. In the proposed algorithm, a pool of classifiers is maintained by the algorithm with each classifier being representative of one single concept. At first, a batch of instances is classified by the algorithm. Thereafter, some of these instances are labeled and this partially labeled batch is used to update the classifiers in the pool. This process repeats for consecutive batches of the streams. The main advantage of the algorithm is that it uses unlabeled instances as well as labeled ones in the learning task. Experimental results show the effectiveness of the proposed algorithm over the state-of-the-art methods, in different aspects. [ABSTRACT FROM AUTHOR]

Incremental learning is a learning algorithm that can get new information from new training sets without forgetting the acquired knowledge from the previously used training sets. In this paper, an incremental learning algorithm based on ensemble learning is proposed. Then, an application of the proposed algorithm for spam filtering is discussed. The proposed algorithm called incremental RotBoost, assumes the environment is stationary. It trains new weak classifiers for newly arriving data, which are added to the ensemble of classifiers. To evaluate the performance of the proposed algorithm, several computer experiments are conducted. The results of computer experiments show the ability of our proposed algorithm for different tasks in the incremental learning. The results also demonstrate that the proposed algorithm can learn incrementally, and it can learn new classes, as well. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive call admission algorithm based on learning automata. The proposed algorithm uses a learning automaton to specify the acceptance/rejection of incoming new calls. It is shown that the given adaptive algorithm converges to an equilibrium point which is also optimal for uniform fractional channel policy. To study the performance of the proposed call admission policy, the computer simulations are conducted. The simulation results show that the level of QoS is satisfied by the proposed algorithm and the performance of given algorithm is very close to the performance of uniform fractional guard channel policy which needs to know all parameters of input traffic. The simulation results also confirm the analysis of the steady-state behaviour. [ABSTRACT FROM AUTHOR]

Expert finding problem in bibliographic networks has received increased interest in recent years. This problem concerns finding relevant researchers for a given topic. Motivated by the observation that rarely do all coauthors contribute to a paper equally, in this paper, we propose two discriminative methods for realizing leading authors contributing in a scientific publication. Specifically, we cast the problem of expert finding in a bibliographic network to find leading experts in a research group, which is easier to solve. We recognize three feature groups that can discriminate relevant experts from other authors of a document. Experimental results on a real dataset, and a synthetic one that is gathered from a Microsoft academic search engine, show that the proposed model significantly improves the performance of expert finding in terms of all common information retrieval evaluation metrics. [ABSTRACT FROM PUBLISHER]

In this paper, we address the problem of scientific-social network integration to find a matching relationship between members of these networks (i.e. The DBLP publication network and the Twitter social network). This task is a crucial step toward building a multi environment expert finding system that has recently attracted much attention in Information Retrieval community. In this paper, the problem of social and scientific network integration is divided into two sub problems. The first problem concerns finding those profiles in one network, which presumably have a corresponding profile in the other network and the second problem concerns the name disambiguation to find true matching profiles among some candidate profiles for matching. Utilizing several name similarity patterns and contextual properties of these networks, we design a focused crawler to find high probable matching pairs, then the problem of name disambiguation is reduced to predict the label of each candidate pair as either true or false matching. Because the labels of these candidate pairs are not independent, state-of-the-art classification methods such as logistic regression and decision tree, which classify each instance separately, are unsuitable for this task. By defining matching dependency graph, we propose a joint label prediction model to determine the label of all candidate pairs simultaneously. Two main types of dependencies among candidate pairs are considered for designing the joint label prediction model which are quite intuitive and general. Using the discriminative approaches, we utilize various feature sets to train our proposed classifiers. An extensive set of experiments have been conducted on six test collection collected from the DBLP and the Twitter networks to show the effectiveness of the proposed joint label prediction model. [ABSTRACT FROM AUTHOR]

Localization is a crucial problem in wireless sensor networks and most of the localization algorithms given in the literature are non-adaptive and designed for fixed sensor networks. In this paper, we propose a learning based localization algorithm for mobile wireless sensor networks. By this technique, mobility in the network will be discovered by two crucial methods in the beacons: position and distance checks methods. These two methods help to have accurate localization and constrain communication just when it is necessary. The proposed method localizes the nodes based on connectivity information (hop count), which doesn't need extra hardware and is cost efficient. The experimental results show that the proposed algorithm is scalable with a small set of beacons in large scale network with a high density of nodes. The given algorithm is fast and free from a pre-deployment requirement. The simulation results show the high performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Estimation of distribution algorithms have evolved as a technique for estimating population distribution in evolutionary algorithms. They estimate the distribution of the candidate solutions and then sample the next generation from the estimated distribution. Bayesian optimization algorithm is an estimation of distribution algorithm, which uses a Bayesian network to estimate the distribution of candidate solutions and then generates the next generation by sampling from the constructed network. The experimental results show that the Bayesian optimization algorithms are capable of identifying correct linkage between the variables of optimization problems. Since the problem of finding the optimal Bayesian network belongs to the class of NP-hard problems, typically Bayesian optimization algorithms use greedy algorithms to build the Bayesian network. This paper proposes a new real-coded Bayesian optimization algorithm for solving continuous optimization problems that uses a team of learning automata to build the Bayesian network. This team of learning automata tries to learn the optimal Bayesian network structure during the execution of the algorithm. The use of learning automaton leads to an algorithm with lower computation time for building the Bayesian network. The experimental results reported here show the preference of the proposed algorithm on both uni-modal and multi-modal optimization problems. [ABSTRACT FROM AUTHOR]

One of the challenging problems in artificial intelligence is credit assignment which simply means distributing the credit among a group, such as a group of agents. We made an attempt to meet this problem with the aid of the reinforcement learning paradigm. In this paper, expertness framework is defined and applied to the multi-agent credit assignment problem. In the expertness framework, the critic agent, who is responsible for distributing credit among agents, is equipped with learning capability, and the proposed credit assignment solution is based on the critic to learn to assign a proportion of the credit to each agent, and the used proportion should be learned by reinforcement learning. The paper also reports the degree of expertness framework robustness and the amount of performance decline in noisy environments. Experimental results show the superiority of the method over the common methods of credit assignment used in lots of different domains and also show that performance reduction with respect to the quantity of the noise is tolerable and the system ultimately converges to the stable and correct behavior, therefore the agents are still capable of efficiently performing in the noisy environments. [ABSTRACT FROM AUTHOR]

Providing an efficient resource allocation mechanism is a challenge to computational grid due to large-scale resource sharing and the fact that Grid Resource Owners (GROs) and Grid Resource Consumers (GRCs) may have different goals, policies, and preferences. In a real world market, various economic models exist for setting the price of grid resources, based on supply-and-demand and their value to the consumers. In this paper, we discuss the use of multiagent-based negotiation model for interaction between GROs and GRCs. For realizing this approach, we designed the Market- and Behavior-driven Negotiation Agents (MBDNAs). Negotiation strategies that adopt MBDNAs take into account the following factors: Competition, Opportunity, Deadline and Negotiator’sTrading Partner’s Previous Concession Behavior. In our experiments, we compare MBDNAs with MDAs (Market-Driven Agent), NDF (Negotiation Decision Function) and Kasbah in terms of the following metrics: total tasks complementation and budget spent. The results show that by taking the proposed negotiation model into account, MBDNAs outperform MDAs, NDF and Kasbah. [ABSTRACT FROM AUTHOR]

Dimensionality reduction is a necessary task in data mining when working with high dimensional data. A type of dimensionality reduction is feature selection. Feature selection based on feature ranking has received much attention by researchers. The major reasons are its scalability, ease of use, and fast computation. Feature ranking methods can be divided into different categories and may use different measures for ranking features. Recently, ensemble methods have entered in the field of ranking and achieved more accuracy among others. Accordingly, in this paper a Heterogeneous ensemble based algorithm for feature ranking is proposed. The base ranking methods in this ensemble structure are chosen from different categories like information theoretic, distance based, and statistical methods. The results of the base ranking methods are then fused into a final feature subset by means of genetic algorithm. The diversity of the base methods improves the quality of initial population of the genetic algorithm and thus reducing the convergence time of the genetic algorithm. In most of ranking methods, it's the user's task to determine the threshold for choosing the appropriate subset of features. It is a problem, which may cause the user to try many different values to select a good one. In the proposed algorithm, the difficulty of determining a proper threshold by the user is decreased. The performance of the algorithm is evaluated on four different text datasets and the experimental results show that the proposed method outperforms all other five feature ranking methods used for comparison. One advantage of the proposed method is that it is independent to the classification method used for classification. [ABSTRACT FROM AUTHOR]

Studying biological networks helps to gain a better understanding of cellular behaviors. One of the prominent models to study complex interactions in biological networks is the Nested Effects Model (NEM). Based on the Nested Effects Model, we propose two methods for inferring signaling pathways from interventional data. In the first method, we search the space of all feasible solutions with an evolutionary approach to maximize a standard Bayesian score. In the second method, sub-models are constructed with informative features and then combined using an averaging method to make the analysis of larger networks computationally possible. We tested our proposed methods in various noise levels on real and artificial networks with different sizes. The networks constructed by our method have a higher level of accuracy compared to the networks inferred by the triplets method introduced by Markowetz. Moreover, our results show a high level of robustness. [ABSTRACT FROM AUTHOR]

Data streams have some unique properties which make them applicable in precise modeling of many real data mining applications. The most challenging property of data streams is the occurrence of 'concept drift'. Recurring concepts is a type of concept drift which can be seen in most of real world problems. Detecting recurring concepts makes it possible to exploit previous knowledge obtained in the learning process. This leads to quick adaptation of the learner whenever a concept reappears. In this paper, we propose a learning algorithm called Pool and Accuracy based Stream Classification with some variations, which takes the advantage of maintaining a pool of classifiers to track recurring concepts. Each classifier is used to describe an existing concept. Consecutive batches of instances are first classified by the pool of classifiers. Two approaches are presented for this task: active classifier and weighted classifiers methods. Then the true labels are revealed and the pool is updated at the end of the batch. Updating the pool is done using one of the following methods: exact Bayesian, Bayesian and Heuristic. As the algorithm may assign multiple classifiers to a single concept, a classifier merging process is used to resolve this problem. Experimental results on real and artificial datasets show the effectiveness of weighted classifiers method while dealing with sudden concept drifting datasets. In addition, the proposed updating methods outperform the existing algorithms in datasets with arbitrary attributes. Finally some performed experiments represent superiority of using merging process in large datasets. [ABSTRACT FROM AUTHOR]

Multiple sequence alignment (MSA) is one of the basic and important problems in molecular biology. MSA can be used for different purposes including finding the conserved motifs and structurally important regions in protein sequences and determine evolutionary distance between sequences. Aligning several sequences cannot be done in polynomial time and therefore heuristic methods such as genetic algorithms can be used to find approximate solutions of MSA problems. Several algorithms based on genetic algorithms have been developed for this problem in recent years. Most of these algorithms use very complicated, problem specific and time consuming mutation operators. In this paper, we propose a new algorithm that uses a new way of population initialization and simple mutation and recombination operators. The strength of the proposed GA is using simple mutation operators and also a special recombination operator that does not have problems of similar recombination operators in other GAs. The experimental results show that the proposed algorithm is capable of finding good MSAs in contrast to existing methods, while it uses simple operators with low computational complexity. [ABSTRACT FROM AUTHOR]

Incremental learning has been used extensively for data stream classification. Most attention on the data stream classification paid on non-evolutionary methods. In this paper, we introduce new incremental learning algorithms based on harmony search. We first propose a new classification algorithm for the classification of batch data called harmony-based classifier and then give its incremental version for classification of data streams called incremental harmony-based classifier. Finally, we improve it to reduce its computational overhead in absence of drifts and increase its robustness in presence of noise. This improved version is called improved incremental harmony-based classifier. The proposed methods are evaluated on some real world and synthetic data sets. Experimental results show that the proposed batch classifier outperforms some batch classifiers and also the proposed incremental methods can effectively address the issues usually encountered in the data stream environments. Improved incremental harmony-based classifier has significantly better speed and accuracy on capturing concept drifts than the non-incremental harmony based method and its accuracy is comparable to non-evolutionary algorithms. The experimental results also show the robustness of improved incremental harmony-based classifier. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new hybrid learning algorithm, called DTGP, to construct cost-sensitive classifiers. This algorithm uses a decision tree as its basic classifier and the constructed decision tree will be pruned by a genetic programming algorithm using a fitness function that is sensitive to misclassification costs. The proposed learning algorithm has been examined through six cost-sensitive problems. The experimental results show that the proposed learning algorithm outperforms in comparison to some other known learning algorithms like C4.5 or naïve Bayesian. [ABSTRACT FROM AUTHOR]

Most email users have experienced spam problems, which have been addressed as text classification problem. In this paper, we propose a novel spam detection method which uses an ensemble of classifiers based on subsampling and dynamic weighted voting techniques. Since there is diversity in genre of emails' contents, the proposed method finds different topics in emails by using a clustering algorithm. The proposed algorithm first extracts disjoint clusters of emails, and then a classifier is trained on each cluster, and finally decisions of classifiers are combined using dynamic weighted majority techniques. In order to classify a new input sample, first it is compared with all cluster centers and its similarity to each cluster is identified; then the classifiers in the vicinity of the input sample obtain greater weights for the final decision of the ensemble. Finally, the outputs of the classifiers are combined using weighted voting with weights calculated from the similarity of the input sample with cluster centers. The experimental results show that the proposed algorithm outperforms pure SVM and the related ensemble based classifiers. [ABSTRACT FROM AUTHOR]

The cellular learning automaton (CLA), which is a combination of cellular automaton (CA) and learning automaton (LA), is introduced recently. This model is superior to CA because of its ability to learn and is also superior to single LA because it is a collection of LAs which can interact with each other. The basic idea of CLA is to use LA to adjust the state transition probability of stochastic CA. Recently, various types of CLA such as synchronous, asynchronous, and open CLAs have been introduced. In some applications such as cellular networks, we need to have a model of CLA for which multiple LAs reside in each cell. In this paper, we study a CLA model for which each cell has several LAs. It is shown that, for a class of rules called commutative rules, the CLA model converges to a stable and compatible configuration. Two applications of this new model such as channel assignment in cellular mobile networks and function optimization are also given. For both applications, it has been shown through computer simulations that CLA-based solutions produce better results. [ABSTRACT FROM AUTHOR]

A solution to channel assignment problem in cellular networks is self-organizing channel assignment algorithm with distributed control. In this paper, we propose three cellular learning automata based dynamic channel assignment algorithms. In the first two algorithms, no information about the status of channels in the whole network will be used by cells for channel assignment whereas in the third algorithm, the additional information regarding status of channels may be gathered and then used by cells in order to allocate channels. The simulation results show that by using the proposed channel assignment algorithms the micro-cellular network can self-organize itself. The simulation results also show that the additional information used by the third algorithm help the cellular learning automata to find an assignment which results in lower blocking probability of calls for the network. [ABSTRACT FROM AUTHOR]

There is no method to determine the optimal topology for multi-layer neural networks for a given problem. Usually the designer selects a topology for the network and then trains it. Since determination of the optimal topology of neural networks belongs to class of NP-hard problems, most of the existing algorithms for determination of the topology are approximate. These algorithms could be classified into four main groups: pruning algorithms, constructive algorithms, hybrid algorithms and evolutionary algorithms. These algorithms can produce near optimal solutions. Most of these algorithms use hill-climbing method and may be stuck at local minima. In this article, we first introduce a learning automaton and study its behaviour and then present an algorithm based on the proposed learning automaton, called survival algorithm, for determination of the number of hidden units of three layers neural networks. The survival algorithm uses learning automata as a global search method to increase the probability of obtaining the optimal topology. The algorithm considers the problem of optimization of the topology of neural networks as object partitioning rather than searching or parameter optimization as in existing algorithms. In survival algorithm, the training begins with a large network, and then by adding and deleting hidden units, a near optimal topology will be obtained. The algorithm has been tested on a number of problems and shown through simulations that networks generated are near optimal. [ABSTRACT FROM AUTHOR]

Cellular learning automata is a combination of learning automata and cellular automata. This model is superior to cellular learning automata because of its ability to learn and also is superior to single learning automaton because it is a collection of learning automata which can interact together. In some applications such as image processing, a type of cellular learning automata in which the action of each cell in the next stage of its evolution not only depends on the local environment (actions of its neighbors) but it also depends on the external environments. We call such a cellular learning automata as open cellular learning automata. In this paper, we introduce open cellular learning automata and then study its steady state behavior. It is shown that for a class of rules called commutative rules, the open cellular learning automata in stationary external environments converges to a stable and compatible configuration. Then the application of this new model to image segmentation has been presented. [ABSTRACT FROM AUTHOR]

In this paper, we first introduce a network of learning automata, which we call it as distributed learning automata and then propose some iterative algorithms for solving stochastic shortest path problem. These algorithms use distributed learning automata to find a policy that determines a path from a source node to a destination node with minimal expected cost (length). In these algorithms, at each stage distributed learning automata determines which edges to be sampled. This sampling method may result in decreasing unnecessary samples and hence decreasing the running time of algorithms. It is shown that the shortest path is found with a probability as close as to unity by proper choice of the parameters of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The cellular learning automata, which is a combination of cellular automata, and learning automata, is a new recently introduced model. This model is superior to cellular automata because of its ability to learn and is also superior to a single learning automaton because it is a collection of learning automata which can interact with each other. The basic idea of cellular learning automata, which is a subclass of stochastic cellular learning automata, is to use the learning automata to adjust the state transition probability of stochastic cellular automata. In this paper, we first provide a mathematical framework for cellular learning automata and then study its convergence behavior. It is shown that for a class of rules, called commutative rules, the cellular learning automata converges to a stable and compatible configuration. The numerical results also confirm the theoretical investigations. [ABSTRACT FROM AUTHOR]

Dropping probability of handoff calls and blocking probability of new calls are two important QoS measures for cellular networks. Since the dropping probability of handoff calls is more important, call admission policies are used to maintain the upper bound of dropping probability of handoff calls. The fractional channel policy (FC) is a general call admission policy and includes most prioritized channel allocation schemes such as guard channel (GC) and limited fractional channel (LFC) policies. In this paper, we introduce a subclass of fractional channel policy, called uniform fractional channel policy (UFC) and study its performance. Expressions for both dropping probability of handoff calls and blocking probability of new calls are derived. Then it is shown that there is an optimal value for the parameter of UFC, which minimizes the blocking probability of new calls with the constraint on the upper bound on the dropping probability of handoff calls. An algorithm for finding the optimal parameter of UFC is also given. Conditions under which the UFC performs better than GC is derived. It is concluded that, the UFC policy performs better than GC policy under the low handoff/new traffic ratio. [ABSTRACT FROM AUTHOR]

Despite of the many successful applications of backpropagation for training multi-layer neural networks, it has many drawbacks. For complex problems it may require a long time to train the networks, and it may not train at all. Long training time can be the result of the non-optimal parameters. It is not easy to choose appropriate value of the parameters for a particular problem. In this paper, by interconnection of fixed structure learning automata (FSLA) to the feedforward neural networks, we apply learning automata (LA) scheme for adjusting these parameters based on the observation of random response of neural networks. The main motivation in using learning automata as an adaptation algorithm is to use its capability of global optimization when dealing with multi-modal surface. The feasibility of proposed method is shown through simulations on three learning problems: exclusive-or, encoding problem, and digit recognition. The simulation results show that the adaptation of these parameters using this method not only increases the convergence rate of learning but it increases the likelihood of escaping from the local minima. [ABSTRACT FROM AUTHOR]