Mansoor Zolghadri - Academia.edu (original) (raw)
Uploads
Papers by Mansoor Zolghadri
Iranian journal of fuzzy systems
This paper considers the automatic design of fuzzy rule-based classification systems based on lab... more This paper considers the automatic design of fuzzy rule-based classification systems based on labeled data. The classification performance and interpretability are of major importance in these systems. In this paper, we utilize the distribution of training patterns in decision subspace of each fuzzy rule to improve its initially assigned certainty grade (i.e. rule weight). Our approach uses a punishment algorithm to reduce the decision subspace of a rule by reducing its weight, such that its performance is enhanced. Obviously, this reduction will cause the decision subspace of adjacent overlapping rules to be increased and consequently rewarding these rules. The results of computer simulations on some well-known data sets show the effectiveness of our approach.
Fuzzy Sets and Systems, 2007
This paper considers the automatic design of fuzzy rule-based classification systems from labeled... more This paper considers the automatic design of fuzzy rule-based classification systems from labeled data. The classification accuracy and interpretability of generated rules are of major importance in fuzzy classification systems. We propose a weighting function for compatibility grade of patterns that improves the performance of fuzzy classification system without degrading the interpretability of fuzzy rules. Our approach divides the covering subspace of each fuzzy rule into two subdivisions based on a threshold. Any pattern with compatibility grade above this threshold should be classified truly so the weighting function enhances their association degree. For patterns below threshold, their compatibility grades remain unchanged. The splitting threshold for each rule (i.e. the compatibility grade of a specific pattern) is found using distribution of patterns in the covering subspace of that rule. We also show that how the proposed approach is applicable when fuzzy rules have certainty grades. Experiments on some well-known data sets are used to evaluate the performance of our approach.
ABSTRACT This paper considers the generation of some interpretable fuzzy rules for assigning an a... more ABSTRACT This paper considers the generation of some interpretable fuzzy rules for assigning an amino acid sequence to the appropriate protein superfamily. Since the main objective of this classifier is the interpretabilitv of rules, we have used the distribution of amino acids in the sequences of proteins as features. These features are the occurrence probabilities of six exchange groups in the sequences. To generate the fuzzy rules, we have used some modified versions of a common approach. The generated rules are simple and understandable, especially for biologists. To evaluate our fuzzv classifiers, we have used four protein superfamilies from the UniProt database. Experimental results show the comprehensibility of the generated fuzzy rules with comparable classification accuracy.
Iranian journal of fuzzy systems
This paper considers the automatic design of fuzzy rule-based classification systems based on lab... more This paper considers the automatic design of fuzzy rule-based classification systems based on labeled data. The classification performance and interpretability are of major importance in these systems. In this paper, we utilize the distribution of training patterns in decision subspace of each fuzzy rule to improve its initially assigned certainty grade (i.e. rule weight). Our approach uses a punishment algorithm to reduce the decision subspace of a rule by reducing its weight, such that its performance is enhanced. Obviously, this reduction will cause the decision subspace of adjacent overlapping rules to be increased and consequently rewarding these rules. The results of computer simulations on some well-known data sets show the effectiveness of our approach.
Fuzzy Sets and Systems, 2007
This paper considers the automatic design of fuzzy rule-based classification systems from labeled... more This paper considers the automatic design of fuzzy rule-based classification systems from labeled data. The classification accuracy and interpretability of generated rules are of major importance in fuzzy classification systems. We propose a weighting function for compatibility grade of patterns that improves the performance of fuzzy classification system without degrading the interpretability of fuzzy rules. Our approach divides the covering subspace of each fuzzy rule into two subdivisions based on a threshold. Any pattern with compatibility grade above this threshold should be classified truly so the weighting function enhances their association degree. For patterns below threshold, their compatibility grades remain unchanged. The splitting threshold for each rule (i.e. the compatibility grade of a specific pattern) is found using distribution of patterns in the covering subspace of that rule. We also show that how the proposed approach is applicable when fuzzy rules have certainty grades. Experiments on some well-known data sets are used to evaluate the performance of our approach.
ABSTRACT This paper considers the generation of some interpretable fuzzy rules for assigning an a... more ABSTRACT This paper considers the generation of some interpretable fuzzy rules for assigning an amino acid sequence to the appropriate protein superfamily. Since the main objective of this classifier is the interpretabilitv of rules, we have used the distribution of amino acids in the sequences of proteins as features. These features are the occurrence probabilities of six exchange groups in the sequences. To generate the fuzzy rules, we have used some modified versions of a common approach. The generated rules are simple and understandable, especially for biologists. To evaluate our fuzzv classifiers, we have used four protein superfamilies from the UniProt database. Experimental results show the comprehensibility of the generated fuzzy rules with comparable classification accuracy.