Fake information, better known as hoaxes, is often found on social media. Currently, media not only used to make friends or socialize with online, but some use it spread hate speech and false information. Hoaxes are very dangerous in life, especially countries large populations ethnically diverse cultures, such Indonesia. Although there have been many studies detecting the accuracy efficiency s...

The departure of good employee incurs direct and indirect cost impacts for an organization. arises from hiring to training the relevant employee. replacement time lost productivity affect running business processes. This work presents use ensemble classifier identify important attributes that affects attrition significantly. data consists related job function, education level, satisfaction towa...

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed for homogeneous ensemble classifiers using bagging and heterogeneous ensemble classifiers using arcing classifier and their performa...

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed for homogeneous ensemble classifiers using bagging and heterogeneous ensemble classifiers using arcing classifier and their performa...

We propose a new classifier combination scheme for the ensemble of classifiers. The Pairwise Fusion Matrix (PFM) constructs confusion matrices based on classifier pairs and thus offers the estimated probability of each class based on each classifier pair. These probability outputs can then be combined and the final outputs of the ensemble of classifiers is reached using various fusion functions...

Currently available algorithms for data stream classification are all designed to handle precise data, while data with uncertainty or imperfection is quite natural and widely seen in real-life applications. Uncertainty can arise in attribute values as well as in class values. In this paper, we focus on the classification of streaming data that has different degrees of uncertainty within class v...

In this project we proposed an ensemble classifier to classify over 20 thousand images sampled from ImageNet, which originally has over 10 million images. One of the challenge of this classification problem is that the images cannot be precisely represented by one type of features, such as SIFT and GIST. Hence, in this project, we use different kinds of features. Another challenge is that diffe...

If a binary decision is taken for each classifier in an ensemble, training patterns may be represented as binary vectors. For a two-class supervised learning problem this leads to a partially specified Boolean function that may be analysed in terms of spectral coefficients. In this paper it is shown that a vote which is weighted by the coefficients enables a fast ensemble classifier that achiev...

We propose a multi-partition, multi-chunk ensemble classifier based data mining technique to classify concept-drifting data streams. Existing ensemble techniques in classifying concept-drifting data streams follow a single-partition, single-chunk approach, in which a single data chunk is used to train one classifier. In our approach, we train a collection of v classifiers from r consecutive dat...