Data Mining is taking out of hidden patterns from huge database. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Nowadays traffic accidents are the major causes of death and injuries i...

Machine learning is a concerned with the design and development of algorithms. Machine learning is a programming approach to computers to achieve optimization .Classification is the prediction approach in data mining techniques. Decision tree algorithm is the most common classifier to build tree because of it is easier to implement and understand. Attribute selection is a concept by which we wa...

Random Forest is an ensemble supervised machine learning technique. Machine learning techniques have applications in the area of Data mining. Random Forest has tremendous potential of becoming a popular technique for future classifiers because its performance has been found to be comparable with ensemble techniques bagging and boosting. Hence, an in-depth study of existing work related to Rando...

Recognizing plants is a vital problem especially for biologists, chemists, and environmentalists. Plant recognition can be performed by human experts manually but it is a time consuming and low-efficiency process. Automation of plant recognition is an important process for the fields working with plants. This paper presents an approach for plant recognition using leaf images. Shape and color fe...

We describe a classifier for predicting message-level sentiment of English microblog messages from Twitter. This paper describes our submission to the SemEval2015 competition (Task 10). Our approach is to combine several variants of our previous year’s SVM system into one meta-classifier, which was then trained using a random forest. The main idea is that the meta-classifier allows the combinat...

In this paper, we evaluate a set of methods for combining features for cover song identification. We first create multiple classifiers based on global tempo, duration, loudness, beats and chroma average features, training a random forest for each feature. Subsequently, we evaluate standard combination rules for merging these single classifiers into a composite classifier based on global feature...