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Linear regression and classification techniques are very common in statistical data analysis but they are often able to extract from data only linear models, which can be a limitation in real data context. Aim of this study is to build an innovative procedure to overcome this defect. Initially, a multiple linear regression analysis using the best-subset algorithm was performed to determine the variables...
This paper proposes an improved Hierarchical Multi-label Classification (HMC) method for solving the gene function prediction. The HMC task is transferred into a series of binary SVM classification tasks. By introducing the hierarchy constraint into learning procedures, two measures with incorporating prior information are implemented to improve the HMC performance. Firstly, for imbalanced functional...
Proteins interact with each other with their binding sites. In this paper, we classify types of interactions using physicochemical properties information of the binding site. We distinguish between permanent protein interactions and transient protein interactions. Basic data of the amino acids on binding sites are retrieved. A method using genetic programming to construct features from the basic data...
The increased number of documents in digital format available on the Web and its useful information for different purposes entail an essential need to organize them. However, this task must be automated in order to save costs and manpower. In the community research, the main approach to face this problem is based on the application of machine learning techniques. This article studies the main machine...
Aiming at the shortages of the existing data-mining model for forecasting the industry security, a classification model based on rough sets and BP neural network (BPNN) is put forward in this paper. First, the theory of rough set is applied to pick up and reduce the index attributes. Then, the training samples are sent to the BPNN to train and learn. After that, the sorts of the coal industry security...
Intrusion detection (ID) is the process of monitoring the events occurring in a computer system or network and analyzing them for signs of intrusions, defined as attempts to compromise the confidentiality, integrity, availability, or to bypass the security mechanisms of a computer or network. Internet services, and the number of Internet users increases every day this makes networks as a window for...
We propose a classification model for the cognitive level of question items in examinations based on Bloom's taxonomy. The model implements the artificial neural network approach, which is trained using the scaled conjugate gradient learning algorithm. Several data preprocessing techniques such as word extraction, stop word removal, stemming, and vector representation are applied to a feature set...
This paper presents a novel method to odor based identification of alcoholic beverages using steady-state responses of a thick film tin oxide sensor array exposed to four different types of whiskies. A neural classifier designed to perform the identification task was trained by incorporating the class information in the training data set in the form of fuzzy entropies of the respective classes. The...
In this paper some different feature extraction methods are compared and their performances in a pattern recognition based P300 detection system are studied. By studying the features in different domains it was concluded that time domain features are more powerful in discriminating P300 signals from non-P300 signals. Therefore, three different sets of features were considered in the time domain and...
Mining data has attracted many researchers because of its usefulness of extracting valuable information from the huge volume of continuously increasing databases. In general using labeled data has been more difficult and time consuming than using unlabeled samples. There are several methods that could be used to build a classifier using unlabeled samples. However these may suffer from poor classification...
This paper presents the use of ANN as a pattern classifier for differential protection of power transformer, which makes the discrimination among normal, magnetizing inrush, over-excitation, external fault and internal fault currents. This scheme has been realized through two ANN architectures, which are designed and trained using feed forward back propagation algorithm with experimental data and...
This paper discusses the application of MFNN for the protection of turbogenerator against internal faults in any winding of the stator. The network has been used as pattern classifier for detection, identification and classification of the internal faults. The full cycle data of simulated fault currents in the phases and their parallel paths have been used for training and testing of proposed neural...
Facial Expression Recognition (FER) from video is an essential research area in the field of Human Computer Interfaces (HCI). In this work, we present a new method to recognize several facial expressions from time sequential facial expression images. Firstly the video sequence is converted to image frames. Sequentially each image frame is subjected to image pre processing. Then the features are extracted...
Stating from the complement between rough sets and decision tree classification algorithm, it proposes a new method of data mining based on rough sets and decision tree classification algorithm, and applies it in the estimating of bank credit risk with the help of a RSES (Rough Set Exploration System) software system for data mining. Experiments have proved that this new method of date mining retains...
One of popular and simple pattern classification algorithms is the k-nearest neighbor rule. However, it often fails to work well when patterns of different classes overlap in some regions in the feature space. To overcome this problem, many researches strive for developing various adaptive or discriminatory metrics to improve its performance for classification, recently. In this paper, we proposed...
This study proposes a novel classification technique of GA/k-prototypes in combination with a genetic algorithm to take the advantage of k-prototypes clustering mechanism for supporting the classification purpose. A genetic algorithm is used to adjust the weight applied to input attributes in order to enable a majority of the data records in each cluster to be with the same outcome class. We conduct...
The county level of basic public services analysis and classification play an important role in county economic growth and improve benefit of healthy development of urbanization in China. According to the county level of basic public services data which is large scale and imbalance, this paper presented a support vector machine model to classify the county level of basic public services. The method...
Most of the countries use bi-script documents. This is because every country uses its own national language and English as second/foreign language. Therefore, bi-lingual document with one language being the English and other being the national language is very common. Postal documents are a very good example of such bi-lingual/script document. This paper deals with word-wise handwritten script identification...
This paper presents a novel approach for recognition of unconstrained handwritten Marathi characters. The recognition is carried out using multistage feature extraction and classification scheme. The initial stages of feature extraction are based upon the structural features and the classification of the characters is done according to their parameters. The final stage of feature extraction employs...
In this paper BLTA is extended to tackle the classification of Semi-Labeled data. BLTA works for Labeled data and perceptron based 4-layered neural network structure is formed. In our proposed extension, this 4-layered neural network structure works for classification of Semi-Labeled data, some samples are labeled and some are unlabeled. Learning algorithm is modified to tackle with such samples....
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