Sharif Digital Repository

As a robust and accurate classification of tumors is necessary for successful treatment of cancer, classification of DNA microarray data has been widely used in successful diagnosis of cancers and some other biological diseases. But the main challenge in classification of microarray data is the extreme asymmetry between the dimensionality of features (usually thousands or even tens of thousands of genes) and that of tissues (few hundreds of samples). Because of such curse of dimensionality, a class prediction model could be very successful in classifying one type of dataset but may fail to perform well in some other ones. Overfitting is another problem that prevents conventional learning... 

In single service wireless cellular networks, two types of call are defined; new call and handoff call. New call blocking probability and handoff call dropping probability are two major parameters of QoS. Some call admission control schemes are proposed for handling new and handoff calls in the cell for keeping these QoS parameters under suitable values. In this work, we will introduce some call admission control schemes and will show performance analysis, advantages and disadvantages of them (under different channel holding times and same channel holding times for new calls and handoff calls). Then we will focus on two schemes and based on their ideas, we will propose a new call admission... 

Decision making often has different effects and results with unequal importance. Most of classifiers try to minimize the rate of misclassified instances. These classifiers assume equal costs for different misclassification types. However, this assumption is not true in many real world problems and different misclassification types have different costs. These differences can be applied by introducing the cost in the process of learning. In this manner, total cost of misclassification will be the evaluation metric of classification. In order to apply this metric to the problems, new learning algorithms are needed. Cost-sensitive learning is the related area of machine learning which deals with... 

Cellular learning automata (CLA) is a distributed computational model that is introduced recently. This model is combination of cellular automata (CA) and learning automata (LA) and is used in many applications such as image processing, channel assignment in cellular networks, VLSI placement, rumor diffusion and modeling of commerce networks, and obtained acceptable results in these applications. This model consists of computational units called cells and each cell has one or more learning automata. In each stage, each automaton chooses an action from its actions set and applies it to the environment. Each cell has some neighboring cells that constitute its local environment. The local rule... 

Worldwide interoperability for Microwave Access (WiMAX) is one of the most dominant cell-based broadband wireless metropolitan access technologies. Packet scheduling algorithm specifies the packet transmission order. In WiMAX standard, packet scheduling algorithm is not defined and its efficient design is left for developers and researchers. The existing researches in the scope of uplink packet scheduling, which is the most challenging packet scheduling scheme, consider only one cell. However the uplink available resources might not be enough when there are many packets that should be scheduled. To solve this problem, we propose an algorithm that uses the load balancing mechanisms that are... 

Nowadays wireless technology is widespread all over the world, because of extensive and successful application of low cost, low power, multifunctional tiny sensor nodes that can be grouped to make up of a wireless sensor network (WSN). Some of Wireless sensor network technologies include Zigbee, EnOcean, Personal area network, Ultra-Wideband and Bluetooth which this thesis focuses on the last one. Scalability, mobility, reliability and energy efficiency are some of the requirements and challenges of WSNs, which among them reliability and reducing energy consumption are two important objectives in wireless sensor networks. Since each sensor node has limited energy to consume, overcoming the... 

Nowadays, multi-agent systems as part of the distributed artificial intelligence play an important role in modeling and solving complex industrial and commercial problems. They have distinguishing characteristics such as distributiveness (spatial, temporal, semantic, or functional distribution), robustness, parallel processing, etc. One of the capabilities that can be added to this system is the learning capability. It can help the system to adapt itself to the new environment. This paper proposed a method for the problem of credit assignment in multi-agent domain. Solving the multi-agent credit assignment problem, one can expect individual learning for a single agent in systems of... 

Text classification is one if the widest and most important applications in data mining. Because of the huge number of features in these applications, a method for dimensionality reduction is needed before applying the classification algorithm. Various number of methods for dimensionality reduction and feature selection are proposed. Feature selection based on feature ranking has received much attention by researchers. The major reasons are their scalability, ease of use, and fast computation. Feature ranking methods are divided to different categories and use different measures for scoring features. Recently ensemble methods have entered the field of ranking, and achieved more accuracy... 

Classification is one of the most important problems in machine learning area. Reliable and successful classification is essential for diagnosing patients for further treatment. In many applications such as bioinformatics unlabeled data is abundant and available. However labeling data is much more difficult and expensive to obtain. This dissertation presents a novel transductive approach for the development of robust microarray data classification. The transduction problem is to estimate the value of classification function at the given points in the working set. This contrasts with the standard inductive learning problem of estimating the classification method at all possible values and... 

One of the standing problems in Molecular Biology and Bioinformatics is uncovering signaling pathways. Discovering the causes of many cancer-like diseases and developing better treatments for them, requires a better understanding of the behavior of cellular processes. Understanding signaling pathways can help to realize cellular processes. Due to the fast increase of possible signaling pathways when the number of components increases, the problem seems to have an inherent complexity. One of the recent methods for generating data relating to such networks is RNA interference technique. In this thesis we use data which are provided by this method. We propose two methods to infer signaling... 

Mobile ad hoc networks (MANETs) are networks which consist entirely of mobile nodes, placed together in ad hoc manner, i.e. with minimal prior planning. In these networks, all nodes have routing capabilities and forward data packets for other nodes. Nodes can enter or leave the network at any time and may also be mobile. Hence, the network topology changes frequently. There are lots of challenges in these networks, which make routing to be a hard task. These challenges arise from the dynamic and unplanned nature of these networks such as unreliability of wireless communication, limited resources available in terms of bandwidth, processing capacity, network size, and etc. Due to these... 

Wireless sensor networks have a wide range of applications in the world. In most of the applications, collected data is not usable without the knowledge about the localization of events. There are two approaches to specifying the location of a sensor: using hardware solutions such as GPS, which is an expensive solution, and using the localization algorithm. Therefore, localization has an important role in sensor networks. Most of the current localization algorithms are non-adaptive and proposed for fixed wireless sensor networks. Recently, adaptive localization algorithms have been considered because of their simple implementation, fast result and low computation overhead for each node. In... 

As part of the definition of concept drift as an online learning task, concepts change or drift as time goes by. Consequently, these changes have to be monitored and their implication for learning should be recognized. An example of concept drift detection is needed for spam filtering problem. An effective spam filter must be able to handle various changes, including changes in the user’s criteria for filtering spam, changes in message topics, and changes caused by the people sending spam messages. In this thesis, spam detection system has been considered in which emails are given sequentially and learns them one by one. As we mentioned, the purpose of this thesis is detecting spam emails.... 

Traditional classification techniques of machine learning assume that data have stationary distributions. This assumption for recent challenges where tremendous amount of data are generated at unprecedented rates with evolving patterns, is not true anymore. Classification of data streams has become an important area of machine learning, as the number of applications facing these challenges increases. Examples of such data streams applications include text streams, surveillance video streams, credit card fraud detection, market basket analysis, information filtering, computer security, etc. An appropriate method for such problems should adapt to drifting concepts by revising and refining the... 

Wireless Sensor Networks (WSNs) have attracted much attention in recent years for their unique characteristics and wide use in many different applications. WSNs are composed of many tiny sensor nodes that have limitations on energy level, bandwidth, processing power and memory. Therefore, reducing energy consumption and the increased network lifetime and scalability are the main routing challenges in sensor networks. Many algorithms were presented for routing in sensor networks; a class of theses algorithms is hierarchical algorithms based on clustering. Their main goals are to reduce energy consumption, distribution energy consumption in the whole network and increasing scalability. There... 

The rapid growth of broadband wireless access (BWA) has increased the demand of new  application  such  as  VoIP,  video  conferencing,  online  gaming  each  of  which  has  different requirement for quality of service. Due to limited bandwidth provided for these networks,  one  of  the  most  important  issues  is  how  effective  we  manage  bandwidth  in  order to support requests. The quality of service is an important indicator of the effective management  of  bandwidth.  Using  mechanisms  of  call  admission  control is  a  commonly  accepted method for balance between quality of service and increase of utilization resource  in  cellular  mobile  networks.  In  fact, ... 

Content-based spam filtering problem is defined as classifying input emails into spam and legitimate emails. so it is considered as an application of supervised-learning. The supervised learning methods often require a large training set of labelled emails to attain good accuracy and the users should label huge amount of emails. In reality, it is not reasonable to expect users to do this. To address this issue and reduce number of labelling request from user active learning techniques can be used. The goal of active Learning algorithms is to achieve appropriate accuracy by using fewer amounts of labelled data in comparison with supervised-learning methods.In this thesis two active learning... 

Increasing information in real world needs the special instrument for data saving, cleaning and processing. Data cleaning is so important steps in machine learning application that include various kind of procedures such as, duplicate detection, fill out missing value and outlier detection. Outliers are observation, which deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism. Many researches has been carried out in the machine learning field with regards to the outlier detection that has applications in real world, like: Intrusion detection for network security, fraud detection in credit cards, fault detection for security in critical... 

Reinforcement learning is a learning method that uses reward and penalty feedbacks, having no information about the right action. In this method, agent gets the state of environment and selects an action among its permissible set of actions, regarding its policy and the given state. Environment, expresses an evaluation, in form of a reinforcement signal and a change in state, as a response for agent’s action. Afterward, the agent updates its policy considering received signal in order to maximize its long term reward. Reinforcement learning rapidly converges to the optimal solution, only if there are few states and actions, but there are lots of domains that consist of too many states and... 

Concept drift is a challenging problem in the context of data stream processing. As a result of increasing applications of data streams, including network intrusion detection, weather forecasting, and detection of unconventional behavior in financial transactions; numerous studies have been conducted in the field of concept drift detection. In order to solve the problem of concept drift detection, an ideal method should be able to quickly and correctly identify a variety of changes, adapt quickly to new concepts, in the presence of limitations of memory and processing power. In this thesis, a new explicit concept drift detection method based on ensemble classifiers has been proposed for data...