Behrouz Minaei

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Papers by Behrouz Minaei

Improving K-Nearest Neighbor Efficacy for FarsiText Classification

by Mohammad Elahimanesh, Behrouz Minaei, and B. Minaei

One of the common processes in the field of text mining is text classification. Because of the co... more One of the common processes in the field of text mining is text classification. Because of the complex nature of Farsi language, words with separate parts and combined verbs, the most of text classification systems are not applicable to Farsi texts. K-Nearest Neighbors (KNN) is one of the most popular used methods for text classification and presents good performance in experiments on different datasets. A method to improve the classification performance of KNN is proposed in this paper. Effects of removing or ...

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Assessment of a novel mass detection algorithm in mammograms

by M. Soryani and Behrouz Minaei

Journal of Cancer Research and Therapeutics, 2013

Context: Mammography is the most effective procedure for an early detection of the breast abnorma... more

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A New Criterion for Clusters Validation

In this paper a new criterion for clusters validation is proposed. This new cluster validation cr... more In this paper a new criterion for clusters validation is proposed. This new cluster validation criterion is used to approximate the goodness of a cluster. The clusters which satisfy a threshold of this measure are selected to participate in clustering ensemble. For combining the chosen clusters, a co-association based consensus function is applied. Since the Evidence Accumulation Clustering method cannot derive the co-association matrix from a subset of clusters, a new EAC based method which is called Extended EAC, EEAC, is applied for constructing the co-association matrix from the subset of clusters. Employing this new cluster validation criterion, the obtained ensemble is evaluated on some well-known and standard data sets. The empirical studies show promising results for the ensemble obtained using the proposed criterion comparing with the ensemble obtained using the standard clusters validation criterion.

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Classification Ensemble by Genetic Algorithms

Icannga, 2011

Different classifiers with different characteristics and methodologies can complement each other ... more Different classifiers with different characteristics and methodologies can complement each other and cover their internal weaknesses; Thus Classifier ensemble is an important approach to handle the drawback. If an automatic and fast method is obtained to approximate the accuracies of different classifiers on a typical dataset, the learning can be converted to an optimization problem and genetic algorithm is an important approach in this way. We proposed a selection method for classification ensemble by applying GA for improving performance of classification. CEGA is examined on some datasets and it considerably shows improvements.

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A New N-gram Feature Extraction-Selection Method for Malicious Code

Icannga, 2011

A Metric to Evaluate a Cluster by Eliminating Effect of Complement Cluster

Proceedings of the 34th Annual German Conference on Advances in Artificial Intelligence, 2011

Improved Face Detection Using Spatial Histogram Features

In this paper, we improve an object detection approach using spatial histogram features, by apply... more In this paper, we improve an object detection approach using spatial histogram features, by applying classifier ensemble. The spatial histogram features can preserve texture and shape information of an object, simultaneously. We train a hierarchical classifier by combining cascade histogram matching and the combination of Multi Layer Perceptrons. The cascade histogram matching is trained via automatically selected discriminative features. A forward sequential selection method is presented to construct uncorrelated and discriminative feature sets for combination of MLPs and RBFs. We evaluate the proposed approach on face objects. Experimental results show that the proposed approach is efficient and robust in object detection.

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An Empirical Study on the Effect of Morphological and Lexical Features in Persian Dependency Parsing

by Mojtaba Khallash and Behrouz Minaei

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Proceedings of LREC'2012 Workshop

by Behrouz Minaei and Mohammad Elahimanesh

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A novel string distance metric for ranking Persian respelling suggestions

Natural Language Engineering

Spelling errors in digital documents are often caused by operational and cognitive mistakes, or b... more Spelling errors in digital documents are often caused by operational and cognitive mistakes, or by the lack of full knowledge about the language of the written documents. Computer-assisted solutions can help to detect and suggest replacements. In this paper, we present a new string distance metric for the Persian language to rank respelling suggestions of a misspelled Persian word by considering the effects of keyboard layout on typographical spelling errors as well as the homomorphic and homophonic aspects of words for orthographical misspellings. We also consider the misspellings caused by disregarded diacritics. Since the proposed string distance metric is custom-designed for the Persian language, we present the spelling aspects of the Persian language such as homomorphs, homophones, and diacritics. We then present our statistical analysis of a set of large Persian corpora to identify the causes and the types of Persian spelling errors. We show that the proposed string distance m...

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A New Hybrid Farsi Text Summarization Technique Based on Term Co-Occurrence and Conceptual Property of the Text

2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008

The importance of text summarization grows rapidly as the amount of information increases exponen... more The importance of text summarization grows rapidly as the amount of information increases exponentially. This paper presents a new hybrid summarization technique that combines statistical properties of documents with Farsi linguistic features. The originality of the technique lies on the use of term co-occurrence property of the text. It could detect the number of subjects. The proposed technique summarizes the document in proportion to the subject treated in a document. It considers the conceptual property of the text algorithm and based on word synonymy prevents similar sentences to be included in the summary. It also preserves the cohesion of the summarized text. Our results show better performance in comparison with FarsiSum, well known Farsi Summarizer, which is based only on the heuristic property of the text and do not consider the Farsi challenges.

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An Innovative Linkage Learning Based on Differences in Local Optimums

Lecture Notes in Computer Science, 2011

Genetic Algorithms (GAs) are categorized as search heuristics and have been broadly applied to op... more Genetic Algorithms (GAs) are categorized as search heuristics and have been broadly applied to optimization problems. These algorithms have been used for solving problems in many applications, but it has been shown that simple GA is not able to effectively solve complex real world problems. For proper solving of such problems, knowing the relationships between decision variables which is referred

A new technique for detecting similar documents based on term co-occurrence and conceptual property of the text

2008 Third International Conference on Digital Information Management, 2008

The importance of detecting similar documents grows rapidly as the amount of information increase... more The importance of detecting similar documents grows rapidly as the amount of information increases exponentially. This paper presents a new technique for identifying similar documents. It combines statistical properties of documents with Persian linguistic features. The proposed technique is mostly suited for detecting similar documents in specific fields. The proposed method is built on lexical chain of important words and based on term co-occurrence property of the text. It prevents the irrelevant documents to be identified similar due to polysemy property of the words. It also considers the order of words in identifying the similar documents. If a document consists of more than one subject, it could also be founded and similar documents according to different topics of the text could be detected. Our results shows improved performance compared to existing wordbased methods like LSI and VSM.

A New N-gram Feature Extraction-Selection Method for Malicious Code

Lecture Notes in Computer Science, 2011

A New Classifier Ensembles Framework

Lecture Notes in Computer Science, 2011

ABSTRACT In constructing a classifier ensemble diversity is more important as the accuracy of its... more ABSTRACT In constructing a classifier ensemble diversity is more important as the accuracy of its elements. To reach a diverse ensemble, one approach is to produce a pool of classifiers. Then we define a metric to evaluate the diversity value in a set of classifiers. We extract a subset of classifiers out of the pool in such a way that has a high diversity value. Usage of Bagging and Boosting as the sources of generators of diversity is another alternative. The third alternative is to partition classifiers and then select a classifier from each partition. Because of high similarity between classifiers of each partition, there is no need to let more than exactly one classifier from each of partition participate in the final ensemble. In this article, the performance of proposed framework is evaluated on some real datasets of UCI repository. Achieved results show effectiveness of the algorithm compare to the original bagging and boosting algorithms.

Improving Reinforcement Learning Agents Using Genetic Algorithms

Lecture Notes in Computer Science, 2010

In this paper a new Reinforcement Learning algorithm was proposed. Q learning is a useful algorit... more

A New Adaptive Framework for Classifier Ensemble in Multiclass Large Data

Lecture Notes in Computer Science, 2011

This paper proposes an innovative combinational algorithm to improve the performance of multiclas... more

Divide & Conquer Classification and Optimization by Genetic Algorithm

2008 Third International Conference on Convergence and Hybrid Information Technology, 2008

In this paper, a new approach for improving the performance of recognition system is proposed. Th... more In this paper, a new approach for improving the performance of recognition system is proposed. The main idea of proposed approach is using pairwise classifiers. Firstly, a multi class classifier is trained and its confusion matrix is derived. Then, the error between metaclasses is derived. In each level, our objective is to minimize the error between metaclasses in the evaluation

CSMD: Centralized Surface Matching Degree, a New Approach for Determining Matching Degree in Fuzzy Systems

2008 Fourth International Conference on Networked Computing and Advanced Information Management, 2008

Generally, the input of fuzzy system can be crisp or fuzzy. Most of fuzzy systems depend on crisp... more Generally, the input of fuzzy system can be crisp or fuzzy. Most of fuzzy systems depend on crisp input while a few of them are based on fuzzy input. The Max-Min algorithm is used to identify matching degree, when the input is fuzzy. This method suffers from drawbacks which are discussed in this paper. We propose an alternative approach which is called Centralized Surface Matching Degree (CSMD). It can remedy all disadvantages of the common approach. This new method applies an adaptive and parametric equation for determining matching degree that can be adapted to any kind of applications.

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Optimizing Fuzzy Cluster Ensemble in String Representation

International Journal of Pattern Recognition and Artificial Intelligence, 2013

In this paper, we present a novel optimization-based method for the combination of cluster ensemb... more In this paper, we present a novel optimization-based method for the combination of cluster ensembles. The information among the ensemble is formulated in 0-1 bit strings. The suggested model de¯nes a constrained nonlinear objective function, called fuzzy string objective function (FSOF), which maximizes the agreement between the ensemble members and minimizes the disagreement simultaneously. Despite the crisp primary partitions, the suggested model employs fuzzy logic in the mentioned objective function. Each row in a candidate solution of the model includes membership degrees indicating how much data point belongs to each cluster. The de¯ned nonlinear model can be solved by every nonlinear optimizer; however; we used genetic algorithm to solve it. Accordingly, three suitable crossover and mutation operators satisfying the constraints of the problem are devised. The proposed crossover operators exchange information between two clusters. They use a novel relabeling method to¯nd corresponding clusters between two partitions. The algorithm is applied on multiple standard datasets. The obtained results show that the modi¯ed genetic algorithm operators are desirable in exploration and exploitation of the big search space.

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