This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to b...

In Text-to-Speech (TTS) systems, prosody phrase prediction is important for the naturalness and intelligibility of synthesized voice. Statistic methods, such as dynamic programming (DP), decision tree (DT), maximum entropy (ME), etc, have been considered for the task. Features based on syntactic and lexical information are widely used. However, the predicted prosody phrases are often observed t...

The proposed method develops a fuzzy rule-based classifier that was tested using features for islanding detection in distributed generation. In the developed technique, the initial classification boundaries are found out by using the decision tree (DT). From the DT classification boundaries, the fuzzy membership functions (MFs) are developed and the corresponding rule base is formulated for isl...

Inverse halftoning (IH) is used to reconstruct the gray image from an input halftone image. This paper presents a machine learning-based IH algorithm to reconstruct the high quality gray images. We first propose a novel variance gain-based tree construction approach to build up an approximate decision tree (DT). Based on the constructed DT, a texture-based training process is presented to const...

In this work, we implemented different models for predicting adsorption separation of a dye from aqueous solution using porous materials. The equilibrium data solute concentrations were collected resources and used in the training verification purposes to develop models. For prediction (Ce), tree models: Multi-layer Perceptron (MLP), Passive aggressive regression, Decision Tree (DT) Regressor. ...

This paper describes aggregated learning models for Complex Word Identification (CWI) task in SemEval 2016. The work focused on selecting the features that determine complexity of words and used different combinations of support vector machine (SVM) and decision tree (DT) techniques for classification. These classifiers were pipelined with pre-processing and postprocessing blocks which helped i...

In this paper we enhance the notion of anomaly detection and use both neural network (NN) and decision tree (DT) for intrusion detection. While DTs are highly successful in detecting known attacks, NNs are more interesting to detect new attacks. In our method we proposed a new approach to design the system using both DT and combination of unsupervised and supervised NN for Intrusion Detection S...

We introduce a parallel approach, "DT-SELECT," for selecting features used by inductive learning algorithms to predict protein secondary structure. DT-SELECT is able to rapidly choose small, nonredundant feature sets from pools containing hundreds of thousands of potentially useful features. It does this by building a decision tree, using features from the pool, that classifies a set of trainin...

We introduce a parallel approach, \DT-Select," for selecting features used by inductive learning algorithms to predict protein secondary structure. DT-Select is able to rapidly choose small, nonre-dundant feature sets from pools containing hundreds of thousands of potentially useful features. It does this by building a decision tree, using features from the pool, that classiies a set of trainin...

Decision tree algorithm (DT) is a commonly used data mining method for classification and regression. DT repeatedly divides dataset into pure subsets based on impurity measurements such as entropy Gini. Then relatively “pure” partitions consisting of observations with the (almost) same class are obtained. Gini index one representative indices measuring data. However, does not take...