Multi-label CLASSIFICATION aims at assigning more than one label to each instance. Many real-world multi-label CLASSIFICATION tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence ALGORITHM based on the binary ALGORITHM of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization ALGORITHM to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed ALGORITHM compared to other ALGORITHMs presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different CLASSIFICATION criteria of multi-label CLASSIFICATION and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

Fingerprint as a biometric has the most applications in verification and identification systems, because of its specific properties. In identification systems, input image is compared with all of images stored in the database. In huge databases, the comparison will take large amounts of time; Consider FBI databases, for instance.Image CLASSIFICATION is one of the approved methods to increase the identification speed. Only one class is assigned to each fingerprint in tradition absolute CLASSIFICATION. Various reasons like noise or lack of all the singularity points in captured region, cause the problem in determination of an absolute class for all the images. In this article, a new method based on probabilistic CLASSIFICATION is presented. In the proposed approach, a set of classes are considered for each input image with a specific probability. These classes are searched in order of their probabilities priority in matching stage.Experiments on well-known FVC2002 database, exhibit the effect of probable CLASSIFICATION clearly. Using only the second and third classes assigned by the proposed method, the identification system achieves about 18% increase in accuracy and 2-3 times speedup in compared to the traditional methods.

In this paper, An IPTV packet CLASSIFICATION ALGORITHM is introduced with adaptive adjustment property and the objective of reducing packet loss ratio in Ethernet passive optical networks. The proposed ALGORITHM, improves weight allocation for WRR scheduling using proper CLASSIFICATION and prioritization of arriving packets to OLT. Based on simulation results with NS2 simulator, the packet loss ratio of proposed ALGORITHM, shows a 65% reduction compared to existing methods which leads to perceptible user quality of experience improvement.

In the last decade, online shopping has played a vital role in the customers' approach to purchase different products, providing convenience to the shops and many benefits for the economy. E-commerce is widely used for digital media products such as movies, images, and software. Thus, the recommendation systems are of great importance, especially in the today's hectic world, which searches for the content that would be interesting to an individual. This research proposes a new two-step recommender system based on the demographic data and user ratings on the public MovieLens datasets. In the first step, clustering on the training dataset is performed based on the demographic data, grouping customers in homogeneous clusters. The clustering includes a hybrid Firefly ALGORITHM (FA) and K-means approach. Due to the FA's ability to avoid trapping into the local optima, which resolves K-means' main pitfall, the combination of these two techniques leads to a much better performance. In the next step, for each cluster, two recommender systems are proposed based on K-Nearest Neighbor (KNN) and Naï, ve Bayesian CLASSIFICATION. The results obtained are evaluated based on many internal and external measures like the Davies-Bouldin index, precision, accuracy, recall, and F-measure. The results obtained show the effectiveness of the K-means/FA/KNN compared with the other extant models.

In recent years, production of text documents has seen an exponential growth, which is the reason why their proper CLASSIFICATION seems necessary for better access. One of the main problems of classifying text documents is working in high-dimensional feature space.Feature Selection (FS) is one of the ways to reduce the number of text attributes. So, working with a great bulk of the feature space without FS increases the computational cost which is a function of the length of the vector, and also, it helps to remove irrelevant attributes. The general approach in this paper combines the hybrid of Flower Pollination ALGORITHM (FPA) with Ada-Boost ALGORITHM. The FPA is used for FS and the Ada-Boost is used for CLASSIFICATION of text documents. Tests were conducted on Reuters-21578, WEBKB and CADE 12 datasets. The results show that the hybrid model has higher detection accuracy in FS compared with Ada-Boost ALGORITHM with model. And comparisons are indicative of higher detection accuracy of the proposed model compared with KNN-K-Means, NB-K-Means and learning models.

Due to the exponential growth of electronic texts, their organization and management requires a tool to provide information and data in search of users in the shortest possible time. Thus, CLASSIFICATION methods have become very important in recent years. In natural language processing and especially text processing, one of the most basic tasks is automatic text CLASSIFICATION. Moreover, text CLASSIFICATION is one of the most important parts in data mining and machine learning. CLASSIFICATION can be considered as the most important supervised technique which classifies the input space to k groups based on similarity and difference such that targets in the same group are similar and targets in different groups are different. Text CLASSIFICATION system has been widely used in many fields, like spam filtering, news CLASSIFICATION, web page detection, Bioinformatics, machine translation, automatic response systems, and applications regarding of automatic organization of documents. The important point in obtaining an efficient text CLASSIFICATION method is extraction and selection of key features of texts. It is proved that only 33% of words and features of the texts are useful and they can be used to extract information and most words existing in texts are used to represent purpose of a text and they are sometimes repeated. Feature selection is known as a good solution to high dimensionality of the feature space. Excessive number of Features not only increase computation time but also degrade CLASSIFICATION accuracy. In general, purpose of extracting and selecting features of texts is to reduce data volume, time required for training, computational time and increase performance speed of the methods proposed for text CLASSIFICATION. Feature extraction refers to the process of generating a small set of new features by combining or transforming the original ones, while in feature selection dimension of the space is reduced by selecting the most prominent features. In this paper, a solution to improve support vector machine ALGORITHM using Imperialism Competitive ALGORITHM, are provided. In this proposed method, the Imperialism Competitive ALGORITHM for selecting features and the support vector machine ALGORITHM for CLASSIFICATION of texts are used. At the stage of extracting the features of the texts, using weighting schemes such as NORMTF, LOGTF, ITF, SPARCK, and TF, each extracted word is allocated a weight in order to determine the role of the words in terms of their effects as the keywords of the texts. The weight of each word indicates the extent of its effect on the main topic of the text compared to other words used in the same text. In the proposed method, the TF weighting scheme is used for attributing weights to the words. In this scheme, the features are a function of the distribution of different features in each of the documents. Moreover, at this stage, using the process of pruning, low-frequency features and words that are used fewer than two times in the text are pruned. Pruning basically filters low-frequency features in a text [18]. In order to reduce the number of dimensions of the features and decrease computational complexity, the imperialist competitive ALGORITHM (ICA) is utilized in the proposed method. The main goal of employing the imperialist competitive ALGORITHM (ICA) in the proposed method is minimizing the loss of data in the texts, while also maximizing the reduction of the dimensions of the features. In the proposed method, since the imperialist competitive ALGORITHM (ICA) has been used for selecting the features, there must be a mapping created between the parameters of the imperialist competitive ALGORITHM (ICA) and the proposed method. Accordingly, when using the imperialist competitive ALGORITHM (ICA) for selecting the key features, the search space includes the dimensions of the features, and among all the extracted features, , , or of all the features are attributed to each of the countries. Since the mapping is carried out randomly, there may be repetitive features in any of the countries as well. Next, based on the general trend of the imperialist competitive ALGORITHM (ICA), some countries which are more powerful are considered as imperialists, while the other countries are considered as colonies. Once the countries are identified, the optimization process can begin. Each country is defined in the form of an array with different values for the variables as in Equations 2 and 3. (2) Country = [, , … , , ] (3) Cost = f (Country) The variables attributed to each country can be structural features, lexical features, semantic features, or the weight of each word, and so on. Accordingly, the power of each country for identifying the class of each text is increased or decreased based on its variables. One of the most important phases of the imperialist competitive ALGORITHM (ICA) is the colonial competition phase. In this phase, all the imperialists try to increase the number of colonies they own. Each of the more powerful empires tries to seize the colonies of the weakest empires to increase their own power. In the proposed method, colonies with the highest number of errors in CLASSIFICATION and the highest number of features are considered as the weakest empires. Based on trial and error, and considering the target function in the proposed method, the number of key features relevant to the main topic of the texts is set to of the total extracted features, and only through using of the key features of each text along with a classifier ALGORITHM such as, support vector machine (SVM), nearest neighbors, and so on, the class of that text can be determined in the proposed method. Since the CLASSIFICATION of texts is a nonlinear problem, in order to classify texts, the problem must first be mapped into a linear problem. In this paper, the RBF kernel function along with is used for mapping the problem. The hybrid ALGORITHM is implemented on the Reuters21578, WebKB, and Cade 12 data sets to evaluate the accuracy of the proposed method. The simulation results indicate that the proposed hybrid ALGORITHM in precision, recall and F Measure criteria is more efficient than primary support machine carriers.