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Machine learning based classifiers used quite often for predicting forest cover types, are the Naïve Bayes classifier, the k-Nearest Neighbors classifier, and the Random forest classifier. This paper is directed towards examining all of these classifiers coupled with feature selection and attribute derivation in order to evaluate which one is best suited for forest cover type classification. Numerous...
Machine learning has received increased interest by both the scientific community and the industry. Most of the machine learning algorithms rely on certain distance metrics that can only be applied to numeric data. This becomes a problem in complex datasets that contain heterogeneous data consisted of numeric and nominal (i.e. categorical) features. Thus the need of transformation from nominal to...
machine learning algorithms are widely used in classification problems. Certainly, recognition quality of algorithms is important indicator, but the ability of the algorithm to learn is more significant. In this work the learning curves experiment was performed in order to identify which of the three learning rates occur when training the machine learning algorithms: overfitting, perfect case and...
Fingerprinting based positioning is commonly used for indoor positioning. In this method, initially a radio map is created using Received Signal Strength (RSS) values that are measured from predefined reference points. During the positioning, the best match between the observed RSS values and existing RSS values in the radio map is established as the predicted position. In the positioning literature,...
Today almost every system or service is dependent on IT systems, and failure of these systems have serious and negative effects on the society. IT incidents are critical for the society as they can stop the function of critical systems and services. Therefore, it is important to analyze these systems for potential risks before becoming dependent on them. Moreover, in a software engineering context...
Extreme learning machine (ELM) is an efficient learning algorithm which can be easily used with least human intervene. But when ELM is applied as multiclass classifier, the results of some classes are not satisfactory and it's hard to adjust the parameters for these classes without affecting other classes. To overcome these limitations, a novel method is proposed. In proposed approach, binary ELM...
This paper presents a generalized pruning extreme learning machine (GP-ELM) algorithm which can generate a compact single-hidden-layer neural network (SLNN) by automatically pruning the number of hidden nodes iteratively while keep high accuracy. The proposed GP-ELM algorithm initializes a SLNN by using extreme learning algorithm (ELM) algorithm given superfluous number of hidden nodes. The following...
MultiBoost ensemble has been well acknowledged as an effective learning algorithm which able to reduce both bias and variance in error and has high generalization performance. However, to deal with the class imbalanced learning, the Multi- Boost shall be amended. In this paper, a new hybrid machine learning method called Distribution based MultiBoost (DBMB) for class imbalanced problems is proposed,...
Today in data mining research we are daily confronted with large amount of data. Most of the time, these data contain redundant and irrelevant data that it is important to extract before a learning task in order to get good accuracy. The fact that today's computers are more powerful does not solves the problems of this ever-growing data. It is therefore crucial to find techniques which allow handling...
Classification algorithms are very important for several fields such as data mining, machine learning, pattern recognition, and other data analysis applications. This work presents the weighted nearest neighbors and fuzzy k-nearest neighbors algorithms to classify chosen medical datasets. This involves several distance functions to calculate the difference between any two instances. Classification...
The general phenomenon for Image Classification is based on the Feature extraction mechanism. In every domain of image analysis, the classification accuracy is dependent on how better the feature set is generated which helps the machine to learn and predict the unknown sample class label. In this paper, a novel feature extraction mechanism is proposed and named as Counting Label Occurrence Matrix...
This paper presents an approach to morphological analysis of Malayalam words as a classification Problem. The idea here is to use Memory Based Language Processing (MBLP) algorithm for Malayalam morphological analysis. MBLP is an approach to language processing based on exemplar storage during learning, and analogical reasoning during processing. The aim of the system is to find the citation forms...
Classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases Now a day's large amount of data is. generated, that need to be analyse, and pattern have to be extracted from that to get some knowledge. Classification is a supervised machine learning task which builds a model from labelled...
Anaphora resolution (AR) is the process of resolving references to an entity in the discourse. The paper presents an algorithm to identify the pronominals and its antecedents in the Malayalam text input. Anaphora resolution is achieved by employing a hybrid of statistical machine learning and rule based approaches. The system is implemented by exploiting the morphological richness of the language...
With the ever increasing production of data from various heterogeneous sources in modern information societies, the need for scalable data-intensive processing is increasing. MapReduce quickly became the de facto framework for large scale data analysis, due to its simple and abstract programming model and its efficient underlying execution system. However, this simplicity comes with a price: its unidirectional...
In this paper, an Extreme Learning Machine (ELM) based technique for Multi-label classification problems is proposed and discussed. In multi-label classification, each of the input data samples belongs to one or more than one class labels. The traditional binary and multi-class classification problems are the subset of the multi-label problem with the number of labels corresponding to each sample...
Building accurate classifiers is difficult when using data that is skewed or imbalanced which is typical of real world data sets. Two popular approaches that have been applied for improving classification accuracy and statistical comparisons of imbalanced data sets are: synthetic minority over-sampling technique (SMOTE) and propensity score matching (PSM). A novel sampling approach is introduced referred...
Training Artificial Neural Networks (ANN) is relatively slow compared to many other machine learning algorithms. In this study, we focus on instance selection to improve training speed. We first evaluate the effectiveness of instance selection algorithms for k-nearest neighbor algorithms with ANN. We then analyze factors in accuracy -- distance from decision boundary, dense regions, and class distributions,...
The objective of the present paper is to demonstrate the potential of Computational Intelligence in applications pertaining to the automatic identification - categorisation of Cardiotocograms using Machine Learning Algorithms and Artificial Neural Networks whose purpose is to distinguish between healthy or pathological cases leading to mortality during birth or fetal cerebral palsy. Interest is also...
In this paper, we hybridize the improved gravitational search algorithm (IGSA) with kernel based extreme learning machine (KELM) method. Based on this, a novel hybrid system IGSA-KELM is proposed to improve the generalization performance for classification problems. In this system, IGSA is designed by combining the search strategy of particle swarm optimization and GSA to effectively reduce the problem...
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