Mohaghegh, S. Noferesti*, and M. Rajaei Abstract: In the era of big data, automatic data analysis techniques such as data mining have been widely used for decision-making and have become very effective. Among data mining techniques, classification is a common method for decision making and prediction. Classification algorithms usually work well on balanced datasets. However, one of the challenges of the classification algorithms is how to correctly predicting the label of new samples based on learning on imbalanced datasets. In this type of dataset, the heterogeneous distribution of the data in different classes causes examples of the minority class to be ignored in the learning process, while this class is more important in some prediction problems. To deal with this issue, in this paper, an efficient method for balancing the imbalanced dataset is presented, which improves the accuracy of the machine learning algorithms to correct prediction of the class label of new samples. According to the evaluations, the proposed method has a better performance compared to other methods based on two common criteria in evaluating the classification of imbalanced datasets, namely "Balanced Accuracy" and "Specificity".

Increasing the use of Internet and some phenomena such as sensor networks has led to an unnecessary increasing the volume of information. Though it has many benefits, it causes problems such as storage space requirements and better processors, as well as data refinement to remove unnecessary data. Data reduction methods provide ways to select useful data from a large amount of duplicate, incomplete and redundant data. These methods are often applied in the pre-processing phase of machine learning algorithms. Three types of data reduction methods can be applied to data: 1. Feature reduction. 2. instance reduction: 3. Discretizing feature values. In this paper, a new algorithm, based on ReliefF, is introduced to decrease both instances and features. The proposed algorithm can run on nominal and numeric features and on data sets with missing values. In addition, in this algorithm, the selection of instances from each class is proportional to the prior probability of classes. The proposed algorithm can run parallel on a multi-core CPU, which decreases the runtime significantly and has the ability to run on big data sets. One type of instance reduction is instance selection. There are many issues in designing instance selection algorithms such as representing the reduced set, how to make a subset of instances, choosing distance function, evaluating designed reduction algorithm, the size of reduced data set and determining the critical and border instances. There are three ways of creating a subset of instances. 1) Incremental. 2) Decremental. 3) Batch. In this paper, we use the batch way for selecting instances. Another important issue is measuring the similarity of instances by a distance function. We use Jaccard index and Manhattan distance for measuring. Also, the decision on how many and what kind of instances should be removed and which must remain is another important issue. The goal of this paper is reducing the size of the stored set of instances while maintaining the quality of dataset. So, we remove very similar and non-border instances in terms of the specified reduction rate. The other type of data reduction that is performed in our algorithm is feature selection. Feature selection methods divide into three categories: wrapper methods, filter methods, and hybrid methods. Many feature selection algorithms are introduced. According to many parameters, these algorithms are divided into different categories; For example, based on the search type for the optimal subset of the features, they can be categorized into three categories: Exponential Search, Sequential Search, and Random Search. Also, an assessment of a feature or a subset of features is done to measure its usefulness and relevance by the evaluation measures that are categorized into various metrics such as distance, accuracy, consistency, information, etc. ReliefF is a feature selection algorithm used for calculating a weight for each feature and ranking features. But this paper is used ReliefF for ranking instances and features. This algorithm works as follows: First, the nearest neighbors of each instances are found. Then, based on the evaluation function, for each instance and feature, a weight is calculated, and eventually, the features and instances that are more weighed are retained and the rest are eliminated. IFSB-ReliefF (instance and Feature selection Based on ReliefF) algorithm is tested on two datasets and then C4. 5 algorithm classifies the reduced data. Finally, the obtained results from the classification of reduced data sets are compared with the results of some instance and feature selection algorithms that are run separately.

It is possible to commission of quasi-intentional felony, the subject of three clauses of article 291, by omission with this condition that the most prominent example of felonies is to be committed by the omission in C section (falseness) of this article. In clause (a) and (b) of this article, the behavior is not effective and can consist of action and omission. The most challenging part of this research is the possibility of commission of simple mistake felony by omission. The commission of simple mistake felony, the subject of clause (a) of Article 292 is not possible by omission, since in this assumption, or the perpetrator is not responsible for the lack of the condition of ability or, in the case of liability, the crime is intentional or quasi-intentional. In clause (b) of this article, if the minor is undertaking in accordance with Article 85 of the Non-Litigious Matters Act, and a felony is committed by commission, this is simple mistake felony. Finally, although the commission of simple mistake felony, the subject of clause (c) of Article 292, is rare by omission, but it cannot be falsified.

Revolution is an extraordinary phenomenon in the development trend of societies. Complexity, multi-dimentionality and multi-layerdness in concept and diversity in its origin, process and instance are among its features. Another feature is that there is no consensus and agreement over a specific and acceptable definition for all schools of thought and approaches.Reviewing these characteristics in this article, the author tries to achieve a comprehensive definition from educational point of view so that when they encounter definitions, students and researchers can have a primary and relatively clear image of the concept. Finally an image of revolution model will be presented from Marxist and Islamic viewpoint.

This paper focuses on the problem of ensemble classification for text-independent speaker verification. Ensemble classification is an efficient method to improve the performance of the classification system. This method gains the advantage of a set of expert classifiers. A speaker verification system gets an input utterance and an identity claim, then verifies the claim in terms of a matching score. This score determines the resemblance of the input utterance and pre-enrolled target speakers. Since there is a variety of information in a speech signal, state-of-the-art speaker verification systems use a set of complementary classifiers to provide a reliable decision about the verification. Such a system receives some scores as input and takes a binary decision: accept or reject the claimed identity. Most of the recent studies on the classifier fusion for speaker verification used a weighted linear combination of the base classifiers. The corresponding weights are estimated using logistic regression. Additional researches have been performed on ensemble classification by adding different regularization terms to the logistic regression formulae. However, there are missing points in this type of ensemble classification, which are the correlation of the base classifiers and the superiority of some base classifiers for each test instance. We address both problems, by an instance based classifier ensemble selection and weight determination method. Our extensive studies on NIST 2004 speaker recognition evaluation (SRE) corpus in terms of EER, min DCF and min CLLR show the effectiveness of the proposed method.

In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classification or training could be reduced. instance-based learning methods are often confronted with the difficulty of choosing the instances, which must be stored to be used during an actual test. Storing too many instances may result in large memory requirements and slow execution speed. In this paper, first, a Distance-based Decision Surface (DDS) is proposed and is used as a separate surface between the classes, and then an instance reduction method, which is based on the DDS is proposed, namely IRDDS (instance Reduction based on Distance-based Decision Surface). Using the DDS with Genetic algorithm selects a reference set for classification. IRDDS selects the most representative instances, satisfying both of the following objectives: high accuracy and reduction rates. The performance of IRDDS is evaluated on real world data sets from UCI repository by the 10-fold cross-validation method. The results of the experiments are compared with some state-of-the-art methods, which show the superiority of the proposed method, in terms of both classification accuracy and reduction percentage.

Content-based image retrieval (CBIR) has received considerable research interest in the recent years. The basic problem in CBIR is the semantic gap between the high-level image semantics and the low-level image features. Region-based image retrieval and learning from user interaction through relevance feedback are two main approaches to solving this problem. Recently, the research in integrating these two major techniques has gained many attentions .The representative is the relevance feedback based Multiple instance Learning mechanism. This paper presents an interactive content-based image retrieval system that incorporates Multiple instance Learning (MIL) into the user relevance feedback to learn the user's subjective visual concepts. The proposed model consists of three main components: The transforming unit (bag generator), the learner unit, and the retrieval unit. In the transforming unit, each image of the database is transformed into the corresponding image bag. The learner unit uses these bags, user's relevance feedbacks and the proposed MIL method to learning user interested visual concepts. In the retrieval unit the images of the database are ranked using a two-phase ranking algorithm. Our model is designed for using in applications that need image retrieval based on the general structures of images such as scene classification and retrieval. We have tested our model on a natural scene image database, consisting of 3000 images taken from the COREL library. Performance is evaluated and the effectiveness ofthe proposed model has been shown through comparative studies.

Transgression, baghy, is applied to the people who, being organized and strengthened, break laws and fight against a just Imam. The proofs necessitating war and confrontation with transgressors in the case of pure Imam, include transgressors in the time of jurisprudent guardianship also. It is, however, necessary to lead them and to reply their objections before acting violently against them. It should be stated that such transgressors cannot be counted as infidel and Islamic governor can let them have their properties. And finally, if the people doing against an Islamic government be of no organization and powers, they can be called but belligerent, not transgressor, with their own judgment.