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By analyzing the disadvantages of the traditional KNN using lazy learning that directly classify the data based on the K neighboring classes using the majority voting method, a new Sigmoid weighted classification algorithm WKS (Weighted KNN Based On Sigmoid) was proposed. WKS provides a new method for learning and training, since each training data di ∊ D contributes to the correct classification...
Now a day's recognition of satellite image authenticity has received too much attention due to the invention of various remote sensing image inpainting algorithms. Satellite image forgery can be referred as a technique in which fake satellite image is generated by the creation and alternation of new image contents. This paper proposes an algorithm for the identification of inpainted remote sensing...
The speed of machine learning has been a concern of the people. The speed of Extreme Learning Machine (ELM) has been improved very faster than others. However, the speed of Sequential Extreme Learning Machine is still slow. So, a fast sequence Extreme Learning Machine (Fast Sequential Extreme Learning Machine, FS-ELM) is present by the use of iterative calculation in calculation of the output weights...
Classifier competence is critical important for dynamic classifier selection. This study proposes a semi-supervised learning algorithm to learn the competence of classifiers under the proposed optimization framework based on graph. First it constructs a graph based on the training data and some unlabeled data. Then it iteratively learns the competence of classifiers. The learned competence not just...
Plant species identification is a digitally challenging object for a better classification such as in taxonomy resources problem. Feature selection as a preprocessing technique in data mining help to identify the prominent attributes of herbal leave with higher dimensioned data set. For this purpose, Relief Feature Selection algorithm was utilized for the improvement of Fuzzy K-Nearest Neighbor (Fuzzy...
Removal of inconsistency from a data set contributes significantly in improving classification accuracy. Inconsistency occurs when attributes of objects have same value but they belong to different classes. Inconsistency is either inherent in the data set or appear during different data preprocessing steps, like discretization, dimensionality reduction and missing value prediction. The aim of the...
Self-healing is an interesting topic in SON (Self- Organizing Networks). In this paper, we investigate cell outage detection problem, and propose an improved TCM (Transductive confidence machines) based automatic cell outage detection algorithm. By incorporating a hypothesis test with the Neyman-Pearson criterion to improve the detection accuracy, the improved TCM can effectively detect cell outage...
In this paper, we propose a new high quality pseudo-relevance feedback documents selection approach that uses machine learning based classifier for selecting a set of good feedback documents for boosting the effectiveness of Query Expansion (QE). Our proposed classification technique utilizes very small amount of labelled data set for training purpose that is very appropriate to select a set of good...
In this paper, a voting based weighted online sequential extreme learning machine (VWOS-ELM) is proposed for class imbalance learning (CIL). VWOS-ELM is the first sequential classifier that can tackle the class imbalance problem in multi-class data streams. Utilizing WOS-ELM and the recently proposed voting based online sequential extreme learning machine (VOS-ELM) method, VWOS-ELM adapts better to...
Data in any form is a valuable resource but more often than not data collected in the real world is completely random and unstructured. Hence, to utilize the true potential of data as a resource we must transform it in such a manner so as to retrieve meaningful information from it. Data mining fulfills this need. Today there is not only a need for efficient data mining techniques to process large...
Scene classification has been studied extensively in the recent past. Most of the state-of-the-art solutions assumed that scene classes are mutually exclusive. However, this is not true as a scene image may belongs to multiple classes and different people are tend to respond inconsistently even given a same scene image. In this paper, we propose a fuzzy qualitative approach to address this problem...
Kernel learning is an important learning framework in machine learning, whose main idea is a mapping from input space to feature space induced by kernel function which yields a linear separation problem in the feature space. However, the generalization ability of kernel learning, which may lead to over-fitting of training data, has not been formally taken into consideration in previous literatures...
This paper focuses on performance evaluation of the classification algorithms for problems of unbalanced and balanced large data sets. Three methods such as ELM, MRAN, and SRAN have been proposed for solving the set classification problem and studied. The ELM is based on randomly chosen hidden nodes and analytically determines the output weights of SLFNs. Then the next method M-RAN is a sequential...
In current developmental research, one of the challenging tasks is to understand the spatio-temporal gene expression patterns and the relationships among different genes. In situ hybridization (ISH) assay which shows mRNA spatio-temporal expression patterns in cells and tissues directly is currently widely utilized in the bench work. With the increasing of available ISH images, automatic annotation...
The performance of a classification model depends not only on the algorithm by which the model is learned, but also on the training set. Manual annotation of the training data is a tedious and time consuming job. In order to overcome the problem of laborious hand-labeling of a large training set, a set of techniques called semi-supervised learning was designed. Co-training is one of the major semi-supervised...
This paper presents a novel classification via aggregated regression algorithm - dubbed CAVIAR - and its application to the OASIS MRI brain image database. The CAVIAR algorithm simultaneously combines a set of weak learners based on the assumption that the weight combination for the final strong hypothesis in CAVIAR depends on both the weak learners and the training data. A regularization scheme using...
A semi-supervised approach for classification of network flows is analyzed and implemented. This traffic classification methodology uses only flow statistics to classify traffic. Specifically, a semi-supervised method that allows classifiers to be designed from training data consisting of only a few labeled and many unlabeled flows. The approach consists of two steps, clustering and classification...
This paper presents an alternative algorithm for integrating the existing knowledge of a supervised learning neural network with the new training data. The algorithm allows the existing knowledge to age out in slow rate as a neural network is gradually retrained with consecutive sets of new samples, resembling the change of application locality under a consistent environment. The algorithm also utilizes...
Linguistic steganography is a technique for hiding information into text carriers. Most of the previous work on linguistic steganography was focused on steganography and there were few researches on steganalysis. Research on attacking methods against linguistic steganography plays an important role in information security (IS) area. In this paper, a linguistic steganography detection algorithm based...
The k-nearest neighbor(k-NN) is improved by applying rough set and distance functions with relearning and ensemble computations to classify data with the higher accuracy values. Then, the proposed relearning and combining ensemble computations are an effective technique for improving accuracy. We develop a new approach to combine kNN classifier based on rough set and distance functions with relearning...
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