decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. the resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. the most comprehensible decision trees have been designed for perfect symbolic data. classical crisp decision trees (dt) are widely applied to classification t...

Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Classical crisp decision trees (DT) are widely applied to classification t...

The research aims to analyze the importance of teaching use unstructured data methods that students generate from learning activities and examine relative efficiency decision trees within load conditions self-efficacy each learner. present collected using a questionnaire cognitive among students. sample included 150 divided into two groups. revealed no significant differences in between groups ...

speaker verification is the process of accepting or rejecting claimed identity in terms of its sound features. a speaker verification system can be used for numerous security systems, including bank account accessing, getting to security points, criminology and etc. when a speaker verification system wants to check the identity of individuals remotely, it confronts problems such as noise effect...

Trypsin is the workhorse protease in mass spectrometry-based proteomics experiments and is used to digest proteins into more readily analyzable peptides. To identify these peptides after mass spectrometric analysis, the actual digestion has to be mimicked as faithfully as possible in silico. In this paper we introduce CP-DT (Cleavage Prediction with Decision Trees), an algorithm based on a deci...

A decision tree is an effective means of data classification from which one can obtain rules that are easy to understand. However, decision trees cannot be conventionally constructed for data which are not explicitly expressed with attribute-value pairs such as graph-structured data. We have proposed a novel algorithm, named Chunkingless Graph-Based Induction (Cl-GBI), for extracting typical pa...

Most existing algorithms for learning Markov network structure either are limited to learning interactions among few variables or are very slow, due to the large space of possible structures. In this paper, we propose three new methods for using decision trees to learn Markov network structures. The advantage of using decision trees is that they are very fast to learn and can represent complex ...

In this article we establish a new method for inducing fuzzy set membership degrees based on empirical training data. The approach is founded on the notion of Redundant Decision Trees (RDT), a generalisation of regular crisp Decision Trees (DT). RDTs suffice in capturing the attribute tests required for recognising crisp concepts, from which the related fuzzy concepts may be unambiguously deriv...

In this paper two machine learning algorithms, Decision Trees (DT) and Hamming Clustering (HC), are compared in building approximated Reliability Expression (RE). The main idea is to employ a classification technique, trained on a restricted subset of data, to produce an estimate of the RE, which provides reasonably accurate values of the reliability. The experiments show that although both met...

A usual way of ASR accuracy evaluation is calculation of Word Error Rate (WER) and Sentence Error Rate (SER). The misrecognitions that contribute to WER are classified into three categories: deletions, insertions and substitutions. The paper presents a study about a contribution of intrinsic speech variabilities to the each of the error category. Decision tree (DT) analysis is used. Five DT sty...